Chapter 18 Part I
0 likes443 views
(1) Forecasting models use historical time series data to predict future trends and patterns. Quantitative forecasting methods are most relevant for this course. (2) Key components of time series include trends, cyclical patterns, seasonality, and irregular fluctuations. Trend lines show gradual shifts, while cyclical components involve recurring patterns above and below trends. (3) Common smoothing methods like moving averages, weighted averages, and exponential smoothing filter out irregular fluctuations to forecast stable time series without strong cycles. These methods are less effective for series with seasonal or cyclical components.
1 of 10
Downloaded 15 times
Ad
Recommended
Time Series Analysis - Modeling and Forecasting
Time Series Analysis - Modeling and ForecastingMaruthi Nataraj K 1) To understand the underlying structure of Time Series represented by sequence of observations by breaking it down to its components.
2) To fit a mathematical model and proceed to forecast the future.
Time series slideshare
Time series slideshareSabbir Tahmidur Rahman This document discusses time series analysis. It defines a time series as a collection of observations made sequentially over time. Examples include financial, scientific, demographic, and meteorological time series data. The document contrasts time series data with cross-sectional data. It also describes the components of a time series, including trends, seasonal variations, cyclical variations, and irregular/random variations. The purposes and uses of time series analysis are discussed, along with methods for decomposing and measuring trends in time series data.
Time series Analysis
Time series AnalysisHabibullah Bahar University College This document defines time series and its components. A time series is a set of observations recorded over successive time intervals. It has four main components: trend, seasonality, cycles, and irregular variations. Trend refers to the overall increasing or decreasing tendency over time. Seasonality refers to predictable changes that occur around the same time each year. Cycles have periods longer than a year. Irregular variations are random fluctuations. The document also discusses methods for analyzing time series components including additive, multiplicative, and mixed models.
Adj Exp Smoothing
Adj Exp Smoothingahmad bassiouny The document discusses adjusted exponential smoothing, a time series forecasting model that accounts for trends in data. It defines the model's equation and components, illustrates how to apply it using sample demand data for portable CD players, and provides an example of how a company could use the model to forecast demand for financial planning purposes. The model smooths random fluctuations in data and provides more accurate forecasts by adjusting for trends over time.
Time series mnr
Time series mnrNH Rao This document discusses quantitative forecasting methods, including time series and causal models. It covers key time series components like trend, seasonality, and cycles. Three main time series methods are described: smoothing, trend projection, and trend projection adjusted for seasonal influence. Moving averages and exponential smoothing are explained as common techniques for forecasting stationary time series. The document also covers decomposing a time series into trend, seasonal, and irregular components. Regression methods are mentioned as another approach when a trend is present in the data.
Trend analysis - Lecture Notes
Trend analysis - Lecture NotesDr. Nirav Vyas Time Series, Classification of Time Series Movements, Trend analysis, Time Series Models, Measurement of Trend
2b. forecasting linear trend
2b. forecasting linear trendSudipta Saha The document discusses techniques for trend forecasting, including linear and nonlinear trends. It provides the equation for a linear trend forecasting model, which uses data points over time to calculate the values of a and b in the equation Ft = a + bt. An example is given showing how to calculate a and b from a dataset and use the linear trend equation to forecast future time periods. Sources of errors in forecasting are also listed, such as an inadequate model, irregular variations in the data, and incorrect use of forecasting techniques.
time series analysis
time series analysisSACHIN AWASTHI This document provides an overview of time series analysis and its key components. It discusses that a time series is a set of data measured at successive times joined together by time order. The main components of a time series are trends, seasonal variations, cyclical variations, and irregular variations. Time series analysis is important for business forecasting, understanding past behavior, and facilitating comparison. There are two main mathematical models used - the additive model which assumes data is the sum of its components, and the multiplicative model which assumes data is the product of its components. Decomposition of a time series involves discovering, measuring, and isolating these different components.
Econometrics
EconometricsQuantUniversity Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
Topics in Econometrics
1634 time series and trend analysis
1634 time series and trend analysisDr Fereidoun Dejahang Time series analysis examines patterns in data over time. It relies on identifying trends, measuring past patterns to forecast the future, and decomposing time series into four main components: secular trends, cyclical movements, seasonal variations, and irregular variations. Secular trends represent long-term direction, while cyclical and seasonal variations have recurring patterns over different time scales. Various techniques can depict trends and identify variations, including freehand drawing, semi-averages, moving averages, least squares, and exponential smoothing.
Time Series
Time Seriesyush313 Time series analysis involves analyzing data collected over time. A time series is a set of data points indexed in time order. The key components of a time series are trends, seasonality, cycles, and irregular variations. Trend refers to the long-term movement of a time series over time. Seasonality refers to periodic fluctuations that occur each year, such as higher sales in winter. Cyclical variations are longer term fluctuations in business cycles. Irregular variations are random, unpredictable fluctuations. Time series analysis is important for forecasting, economic analysis, and business planning. Common methods for analyzing time series components include moving averages, least squares regression, decomposition models, and harmonic analysis.
Mba 532 2011_part_3_time_series_analysis
Mba 532 2011_part_3_time_series_analysisChandra Kodituwakku This document provides an overview of time series analysis and forecasting techniques. It discusses key concepts such as stationary and non-stationary time series, additive and multiplicative models, smoothing methods like moving averages and exponential smoothing, autoregressive (AR), moving average (MA) and autoregressive integrated moving average (ARIMA) models. The document uses examples to illustrate how to identify patterns in time series data and select appropriate models for description, explanation and forecasting of time series.
Time series analysis- Part 2
Time series analysis- Part 2QuantUniversity The document summarizes a time series analysis workshop presented by Sri Krishnamurthy on December 20, 2018 in Boston. The workshop was hosted by QuantUniversity, which provides data science and quantitative finance programs and advisory services. Upcoming events from QuantUniversity include time series analysis and machine learning workshops in early 2019.
Lesson08_static11
Lesson08_static11thangv This document discusses various techniques for time-series analysis and forecasting, including decomposition methods, smoothing methods like moving averages, exponential smoothing, and trend and autoregressive models. It covers identifying components like trend and seasonality, fitting linear, quadratic and exponential trend models, developing autoregressive models of different orders, and selecting the appropriate forecasting model based on residual analysis and model simplicity.
Trend adjusted exponential smoothing forecasting metho ds
Trend adjusted exponential smoothing forecasting metho dsKiran Hanjar This document describes the trend adjusted exponential smoothing forecasting method. It is a quantitative time series forecasting technique that calculates the weighted average of the current actual value and previous forecast, with an adjustment made for any trend present in the data. The trend adjustment is calculated using a smoothing constant and prior trend value. The method is preferred when a trend or seasonal pattern is evident in historical data. An example is provided to illustrate how to compute forecasts using this method by calculating the unadjusted forecast, trend, and adjusted forecast over multiple time periods.
Demand forecasting by time series analysis
Demand forecasting by time series analysisSunny Gandhi Demand is a buyer's willingness and ability to pay for a product or service. Demand forecasting estimates the quantity of a product that consumers will purchase. It is important for resource distribution, production planning, pricing decisions, and reducing business risk. Demand forecasting can be done at the micro, industry, or macro level. Common forecasting methods include time series analysis of historical sales data, market testing, and qualitative techniques like educated guesses. Accurate, plausible, simple, and durable demand forecasts are ideal.
Time Series, Moving Average
Time Series, Moving AverageSOMASUNDARAM T This document discusses time series analysis and its key components. It begins by defining a time series as a sequence of data points measured over successive time periods. The four main components of a time series are identified as: 1) Trend - the long-term pattern of increase or decrease, 2) Seasonal variations - repeating patterns over 12 months, 3) Cyclical variations - fluctuations lasting more than a year, and 4) Irregular variations - unpredictable fluctuations. Two common methods for measuring trends are introduced as the moving average method and least squares method. Formulas and examples are provided for calculating trend values using these techniques.
Time series
Time seriesMuhammad Umar This document discusses time series analysis and its various components. It defines a time series as a set of observations made over time, usually at regular intervals. The key components of a time series are the secular trend, seasonal variation, cyclical variation, and irregular variation. Several methods are discussed for measuring and decomposing these components, including free hand curve, semi-average, moving average, and least squares methods. The importance of time series analysis for forecasting and planning is also highlighted. Time series analysis decomposes a time series into its various components to help understand the data better and make predictions.
Exponential smoothing
Exponential smoothingJairo Moreno This document discusses exponential smoothing techniques for time series forecasting. It introduces simple, double, and triple exponential smoothing. Simple exponential smoothing works for stationary time series, double exponential smoothing adds a trend component for trending time series, and triple exponential smoothing (the Holt-Winters method) further adds seasonal components to handle seasonality. The document discusses parameters, components, extensions, and evaluation metrics for exponential smoothing models.
Time series
Time seriesNadeem Uddin This document defines time series and provides examples of time series data. It discusses the components of time series, including trends, seasonal variations, cyclical variations, and random variations. Graphs of time series are presented, along with uses of time series analysis such as prediction and comparison. Mathematical models for time series analysis, including additive and multiplicative models, are also introduced.
Trend and seasonal components/Abshor Marantika/Cindy Aprilia Anggaresta
Trend and seasonal components/Abshor Marantika/Cindy Aprilia AnggarestaCindyAprilia15 The document discusses time series decomposition, which involves separating a time series into different components: trend, seasonal, and irregular.
The trend component represents long-term increases or decreases in the data. The seasonal component captures regular fluctuations that occur with a fixed periodicity, such as monthly or yearly patterns. The irregular component represents stationary random fluctuations.
Time series can be decomposed additively, by adding the trend, seasonal, and irregular components, or multiplicatively, by multiplying them. The appropriate method depends on whether seasonal fluctuations vary with the overall level of the series. Decomposition aims to produce stationary irregular components that can be modeled.
Time series
Time seriesHaitham Ahmed This document provides an overview of time series analysis and forecasting using neural networks. It discusses key concepts like time series components, smoothing methods, and applications. Examples are provided on using neural networks to forecast stock prices and economic time series. The agenda covers introduction to time series, importance, components, smoothing methods, applications, neural network issues, examples, and references.
Quantitative forecasting
Quantitative forecastingRavi Loriya This document discusses quantitative forecasting methods. Quantitative forecasting depends on data and analytical techniques to predict future demand based on past demand information. Some common quantitative forecasting methods discussed include time series analysis, causal models, and simulation. Time series methods like simple moving averages, weighted moving averages, and exponential smoothing are explained as techniques to forecast future demand based on historical data trends. Linear regression models are also mentioned as a way to establish relationships between demand and other factors. Key factors that influence the selection of a forecasting method include data availability, required time horizon, accuracy needs, and available resources.
Exponential Weighting Moving Average.
Exponential Weighting Moving Average.Syed Waqar Hussain Shah The document discusses different types of moving averages used for demand forecasting, including simple moving average, weighted moving average, and exponential weighted moving average. An exponential weighted moving average assigns greater weight and importance to more recent data points compared to older data. The exponential weighted moving average model was adapted by Holt and Winter to include a trend term that allows it to capture changes over time. The model calculates the weighted average using a smoothing factor lambda between 0 and 1, with larger lambda values giving more weight to recent data.
Lesson08_new
Lesson08_newshengvn This document discusses techniques for time-series analysis and forecasting including smoothing data using moving averages and exponential smoothing, fitting trend lines using linear, quadratic and exponential models, and using autoregressive models. It describes choosing the appropriate forecasting model by analyzing residuals and selecting the simplest model that adequately fits the data.
Deseasonalizing Forecasts
Deseasonalizing Forecastsahmad bassiouny The document discusses seasonal adjustment methods for time series forecasts. It defines seasonality and explains common causes of seasonal patterns. The main seasonal adjustment method described is a four-step process: 1) forecast demand values, 2) calculate demand/forecast ratios, 3) average ratios to determine seasonal indices, 4) adjust forecasts by multiplying them by seasonal indices. An example is provided to illustrate applying this method to quarterly widget demand data.
Presentation 3
Presentation 3uliana8 This document discusses different average-based forecasting methods including simple average, moving average, weighted average, and cumulative average. It provides examples and formulas for calculating forecasts for each method. The simple average method calculates the forecast as the average of all past data points. The moving average method uses the average of the most recent data points. The weighted average assigns different weights to each data point. The cumulative average adds the most recent actual value to the cumulative sum of weighted past increases.
Chapter 15
Chapter 15Matthew L Levy This document summarizes key concepts from Chapter 15 of ISDS 2001 regarding multiple regression models. It explains that multiple regression extends simple linear regression to include more than one independent variable. The multiple regression equation is presented as y = β0 + β1x1 + β2x2 + ... + βpxp + ε. Estimates of the coefficients (b0, b1, etc.) are obtained using least squares to minimize the difference between actual and predicted y-values. Interpretation of the coefficients changes in multiple regression since each represents the effect of a single independent variable while holding others constant. Assumptions and testing procedures like the F-test and t-test are also discussed.
File storageandbackupsystems
File storageandbackupsystemsMicheline LeBlanc The document discusses file storage and backup systems. It defines files and folders, describes common file storage devices like hard drives, optical disks, solid state drives and memory sticks. It emphasizes the importance of backing up files regularly using either manual backups, backup applications, automatic backup systems or cloud backup services to protect against data loss from hardware failure. Simple backup strategies and resources for learning more are provided.
Chapter 4 R Part I
Chapter 4 R Part IMatthew L Levy This document provides an overview of key concepts in decision analysis, including:
- Problem formulation involves identifying decision alternatives and possible outcomes (states of nature) and their consequences. Influence diagrams and payoff tables can be used to represent this information graphically.
- Decision trees show the logical progression of decisions and outcomes over time using nodes connected by branches. They allow calculating expected values when probabilities of states of nature are known.
- When probabilities are unknown, three approaches can be used: optimistic (based on best payoff), conservative (based on worst payoff), and minimax regret (minimizes difference between best and current alternative).
- Expected value is the probability-weighted sum of payoffs and
Ad
More Related Content
What's hot (19)
Econometrics
EconometricsQuantUniversity Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.
In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
Topics in Econometrics
1634 time series and trend analysis
1634 time series and trend analysisDr Fereidoun Dejahang Time series analysis examines patterns in data over time. It relies on identifying trends, measuring past patterns to forecast the future, and decomposing time series into four main components: secular trends, cyclical movements, seasonal variations, and irregular variations. Secular trends represent long-term direction, while cyclical and seasonal variations have recurring patterns over different time scales. Various techniques can depict trends and identify variations, including freehand drawing, semi-averages, moving averages, least squares, and exponential smoothing.
Time Series
Time Seriesyush313 Time series analysis involves analyzing data collected over time. A time series is a set of data points indexed in time order. The key components of a time series are trends, seasonality, cycles, and irregular variations. Trend refers to the long-term movement of a time series over time. Seasonality refers to periodic fluctuations that occur each year, such as higher sales in winter. Cyclical variations are longer term fluctuations in business cycles. Irregular variations are random, unpredictable fluctuations. Time series analysis is important for forecasting, economic analysis, and business planning. Common methods for analyzing time series components include moving averages, least squares regression, decomposition models, and harmonic analysis.
Mba 532 2011_part_3_time_series_analysis
Mba 532 2011_part_3_time_series_analysisChandra Kodituwakku This document provides an overview of time series analysis and forecasting techniques. It discusses key concepts such as stationary and non-stationary time series, additive and multiplicative models, smoothing methods like moving averages and exponential smoothing, autoregressive (AR), moving average (MA) and autoregressive integrated moving average (ARIMA) models. The document uses examples to illustrate how to identify patterns in time series data and select appropriate models for description, explanation and forecasting of time series.
Time series analysis- Part 2
Time series analysis- Part 2QuantUniversity The document summarizes a time series analysis workshop presented by Sri Krishnamurthy on December 20, 2018 in Boston. The workshop was hosted by QuantUniversity, which provides data science and quantitative finance programs and advisory services. Upcoming events from QuantUniversity include time series analysis and machine learning workshops in early 2019.
Lesson08_static11
Lesson08_static11thangv This document discusses various techniques for time-series analysis and forecasting, including decomposition methods, smoothing methods like moving averages, exponential smoothing, and trend and autoregressive models. It covers identifying components like trend and seasonality, fitting linear, quadratic and exponential trend models, developing autoregressive models of different orders, and selecting the appropriate forecasting model based on residual analysis and model simplicity.
Trend adjusted exponential smoothing forecasting metho ds
Trend adjusted exponential smoothing forecasting metho dsKiran Hanjar This document describes the trend adjusted exponential smoothing forecasting method. It is a quantitative time series forecasting technique that calculates the weighted average of the current actual value and previous forecast, with an adjustment made for any trend present in the data. The trend adjustment is calculated using a smoothing constant and prior trend value. The method is preferred when a trend or seasonal pattern is evident in historical data. An example is provided to illustrate how to compute forecasts using this method by calculating the unadjusted forecast, trend, and adjusted forecast over multiple time periods.
Demand forecasting by time series analysis
Demand forecasting by time series analysisSunny Gandhi Demand is a buyer's willingness and ability to pay for a product or service. Demand forecasting estimates the quantity of a product that consumers will purchase. It is important for resource distribution, production planning, pricing decisions, and reducing business risk. Demand forecasting can be done at the micro, industry, or macro level. Common forecasting methods include time series analysis of historical sales data, market testing, and qualitative techniques like educated guesses. Accurate, plausible, simple, and durable demand forecasts are ideal.
Time Series, Moving Average
Time Series, Moving AverageSOMASUNDARAM T This document discusses time series analysis and its key components. It begins by defining a time series as a sequence of data points measured over successive time periods. The four main components of a time series are identified as: 1) Trend - the long-term pattern of increase or decrease, 2) Seasonal variations - repeating patterns over 12 months, 3) Cyclical variations - fluctuations lasting more than a year, and 4) Irregular variations - unpredictable fluctuations. Two common methods for measuring trends are introduced as the moving average method and least squares method. Formulas and examples are provided for calculating trend values using these techniques.
Time series
Time seriesMuhammad Umar This document discusses time series analysis and its various components. It defines a time series as a set of observations made over time, usually at regular intervals. The key components of a time series are the secular trend, seasonal variation, cyclical variation, and irregular variation. Several methods are discussed for measuring and decomposing these components, including free hand curve, semi-average, moving average, and least squares methods. The importance of time series analysis for forecasting and planning is also highlighted. Time series analysis decomposes a time series into its various components to help understand the data better and make predictions.
Exponential smoothing
Exponential smoothingJairo Moreno This document discusses exponential smoothing techniques for time series forecasting. It introduces simple, double, and triple exponential smoothing. Simple exponential smoothing works for stationary time series, double exponential smoothing adds a trend component for trending time series, and triple exponential smoothing (the Holt-Winters method) further adds seasonal components to handle seasonality. The document discusses parameters, components, extensions, and evaluation metrics for exponential smoothing models.
Time series
Time seriesNadeem Uddin This document defines time series and provides examples of time series data. It discusses the components of time series, including trends, seasonal variations, cyclical variations, and random variations. Graphs of time series are presented, along with uses of time series analysis such as prediction and comparison. Mathematical models for time series analysis, including additive and multiplicative models, are also introduced.
Trend and seasonal components/Abshor Marantika/Cindy Aprilia Anggaresta
Trend and seasonal components/Abshor Marantika/Cindy Aprilia AnggarestaCindyAprilia15 The document discusses time series decomposition, which involves separating a time series into different components: trend, seasonal, and irregular.
The trend component represents long-term increases or decreases in the data. The seasonal component captures regular fluctuations that occur with a fixed periodicity, such as monthly or yearly patterns. The irregular component represents stationary random fluctuations.
Time series can be decomposed additively, by adding the trend, seasonal, and irregular components, or multiplicatively, by multiplying them. The appropriate method depends on whether seasonal fluctuations vary with the overall level of the series. Decomposition aims to produce stationary irregular components that can be modeled.
Time series
Time seriesHaitham Ahmed This document provides an overview of time series analysis and forecasting using neural networks. It discusses key concepts like time series components, smoothing methods, and applications. Examples are provided on using neural networks to forecast stock prices and economic time series. The agenda covers introduction to time series, importance, components, smoothing methods, applications, neural network issues, examples, and references.
Quantitative forecasting
Quantitative forecastingRavi Loriya This document discusses quantitative forecasting methods. Quantitative forecasting depends on data and analytical techniques to predict future demand based on past demand information. Some common quantitative forecasting methods discussed include time series analysis, causal models, and simulation. Time series methods like simple moving averages, weighted moving averages, and exponential smoothing are explained as techniques to forecast future demand based on historical data trends. Linear regression models are also mentioned as a way to establish relationships between demand and other factors. Key factors that influence the selection of a forecasting method include data availability, required time horizon, accuracy needs, and available resources.
Exponential Weighting Moving Average.
Exponential Weighting Moving Average.Syed Waqar Hussain Shah The document discusses different types of moving averages used for demand forecasting, including simple moving average, weighted moving average, and exponential weighted moving average. An exponential weighted moving average assigns greater weight and importance to more recent data points compared to older data. The exponential weighted moving average model was adapted by Holt and Winter to include a trend term that allows it to capture changes over time. The model calculates the weighted average using a smoothing factor lambda between 0 and 1, with larger lambda values giving more weight to recent data.
Lesson08_new
Lesson08_newshengvn This document discusses techniques for time-series analysis and forecasting including smoothing data using moving averages and exponential smoothing, fitting trend lines using linear, quadratic and exponential models, and using autoregressive models. It describes choosing the appropriate forecasting model by analyzing residuals and selecting the simplest model that adequately fits the data.
Deseasonalizing Forecasts
Deseasonalizing Forecastsahmad bassiouny The document discusses seasonal adjustment methods for time series forecasts. It defines seasonality and explains common causes of seasonal patterns. The main seasonal adjustment method described is a four-step process: 1) forecast demand values, 2) calculate demand/forecast ratios, 3) average ratios to determine seasonal indices, 4) adjust forecasts by multiplying them by seasonal indices. An example is provided to illustrate applying this method to quarterly widget demand data.
Presentation 3
Presentation 3uliana8 This document discusses different average-based forecasting methods including simple average, moving average, weighted average, and cumulative average. It provides examples and formulas for calculating forecasts for each method. The simple average method calculates the forecast as the average of all past data points. The moving average method uses the average of the most recent data points. The weighted average assigns different weights to each data point. The cumulative average adds the most recent actual value to the cumulative sum of weighted past increases.
Viewers also liked (19)
Chapter 15
Chapter 15Matthew L Levy This document summarizes key concepts from Chapter 15 of ISDS 2001 regarding multiple regression models. It explains that multiple regression extends simple linear regression to include more than one independent variable. The multiple regression equation is presented as y = β0 + β1x1 + β2x2 + ... + βpxp + ε. Estimates of the coefficients (b0, b1, etc.) are obtained using least squares to minimize the difference between actual and predicted y-values. Interpretation of the coefficients changes in multiple regression since each represents the effect of a single independent variable while holding others constant. Assumptions and testing procedures like the F-test and t-test are also discussed.
File storageandbackupsystems
File storageandbackupsystemsMicheline LeBlanc The document discusses file storage and backup systems. It defines files and folders, describes common file storage devices like hard drives, optical disks, solid state drives and memory sticks. It emphasizes the importance of backing up files regularly using either manual backups, backup applications, automatic backup systems or cloud backup services to protect against data loss from hardware failure. Simple backup strategies and resources for learning more are provided.
Chapter 4 R Part I
Chapter 4 R Part IMatthew L Levy This document provides an overview of key concepts in decision analysis, including:
- Problem formulation involves identifying decision alternatives and possible outcomes (states of nature) and their consequences. Influence diagrams and payoff tables can be used to represent this information graphically.
- Decision trees show the logical progression of decisions and outcomes over time using nodes connected by branches. They allow calculating expected values when probabilities of states of nature are known.
- When probabilities are unknown, three approaches can be used: optimistic (based on best payoff), conservative (based on worst payoff), and minimax regret (minimizes difference between best and current alternative).
- Expected value is the probability-weighted sum of payoffs and
Chapter 14 Part Ii
Chapter 14 Part IiMatthew L Levy This document summarizes key concepts from Chapter 14 of the textbook on using simple linear regression for estimation and prediction. It discusses:
1) Developing point estimates, confidence intervals, and prediction intervals using the regression equation for estimating mean and individual y values. Confidence intervals estimate mean y values while prediction intervals are wider and estimate individual y values.
2) Residual analysis to check assumptions of the regression model by examining residual plots against x and predicted y values, as well as standardized residual and normal probability plots.
3) Identifying influential outliers and leverage points, which can impact the model, using standardized residuals and leverage values.
Wireless Technology
Wireless Technologyrromro The document provides a brief timeline of wireless communication technologies including wireless telegraphy, wireless telephony, amateur radio use during World War 1, the Radio Act of 1912 which led to the formation of RCA in 1919, the development of wireless broadcasting and radio television, and the invention of vacuum tubes and transistors which enabled computers. The timeline hits the key developments and inventions that advanced wireless communication over the early 20th century.
Present, Past and Future of the Telephony in Mexico
Present, Past and Future of the Telephony in Mexicorromro The document discusses the past, present, and future of telephony in Mexico from 1882 to 2009. In the past, telephony grew slowly from 1882 to 1972 due to high costs and a Telmex monopoly. From 1995 to 2009, pre-paid mobile phones grew in popularity, reaching 13% of the population. Currently, Telmex dominates the infrastructure with 90% market share, while mobile phone providers include Telcel at 80% and Movistar at 13%. Looking ahead, continued growth of affordable mobile services is expected to drive further adoption of telephony in Mexico from 2008 onward.
Chapter 16
Chapter 16Matthew L Levy The document discusses the general linear model (GLM) as an extension of simple and multiple linear regression models. It describes how the GLM allows for modeling more complex relationships between variables by including transformed, squared, and interaction terms. Specifically, it explains how curvilinear relationships can be modeled by adding squared terms and how interaction effects between two variables can be modeled by including an interaction term. The document also discusses transforming the dependent variable to correct for non-constant variance.
Story Board Consejo South Park
Story Board Consejo South Parkrromro South Park is a dangerous and poor area of Seattle with high rates of gang and drug crimes. However, there is one organization called Consejo that works to help youth stay away from gangs and drugs. The staff of Consejo pick youth up from school, organize activities like art, and generally provide support and alternatives to crime. Both youth and their families have credited Consejo with positively changing lives and helping to calm the community.
Chapter 14R
Chapter 14RMatthew L Levy This document summarizes key concepts about waiting line models:
1. Waiting line models use queueing theory to help managers understand and make decisions about operating characteristics of waiting lines like average wait times and number of customers in the system.
2. Common characteristics of waiting line systems include single channel lines, random and independent customer arrivals modeled by the Poisson distribution, and exponentially distributed service times.
3. The document outlines formulas for calculating operating characteristics of single and multiple channel models based on arrival and service rates when arrivals follow Poisson and service times are exponential.
Chapter 15R Lecture
Chapter 15R LectureMatthew L Levy This document provides an overview of simulation as a quantitative approach to decision making under uncertainty. It discusses using simulation to model systems and experiment with different inputs to understand an actual system. It also covers using simulation for risk analysis by generating a profit model with probabilistic inputs and controllable factors to analyze best, worst, and base cases. The document concludes by introducing dynamic simulation models for waiting line situations where arrivals and departures occur over discrete time periods.
Presentation Week 2
Presentation Week 2rromro Social and technological networks are converging. The document discusses how social media platforms allow for connecting with others through sharing content and comments online. It may raise questions about how social relationships are developing through these new technological means of interaction and information sharing.
Chapter 5R
Chapter 5RMatthew L Levy This document summarizes key concepts from Chapter 5R, which discusses utility and game theory in decision making. Utility refers to the total worth of an outcome and reflects factors like profit, risk, and loss. It is important when outcomes are extreme or involve significant downside risk. The chapter introduces steps for assigning utility values to monetary outcomes to incorporate risk preferences into decisions. It also distinguishes between risk-averse, risk-neutral, and risk-seeking attitudes. The second part defines game theory and zero-sum games involving competitive strategies between two players. Optimal strategies are determined using maximin and minimax approaches. Examples illustrate competitive market share scenarios that can be modeled as games.
Uwtwtrbook Tweeting In Latin America
Uwtwtrbook Tweeting In Latin Americarromro This document summarizes Twitter usage in Latin America, highlighting that Brazil, Mexico, Argentina, and Chile have among the largest Twitter markets. It identifies newspapers, radio, retail companies like Jumbo and Falabella, and politicians like Marco Enriquez-Ominami and Sebastian Piñera as top Twitter sectors. It also shows the exponential growth in followers for the Twitter account of Marco Enriquez-Ominami from June to August as he ran for president.
Chapter 14 Part I
Chapter 14 Part IMatthew L Levy Regression analysis is used to model relationships between variables. Simple linear regression involves modeling the relationship between a single independent variable and dependent variable. The regression equation estimates the dependent variable (y) as a linear function of the independent variable (x). The parameters β0 and β1 are estimated using the method of least squares. The coefficient of determination (r2) measures how well the regression line fits the data. Additional tests like the t-test, confidence intervals, and F-test are used to test if the independent variable significantly predicts the dependent variable. While these tests can indicate a statistically significant relationship, they do not prove causation.
MAGRAY- A MARTIAL KASHMIRI TRIBE/CASTE/RACE
MAGRAY- A MARTIAL KASHMIRI TRIBE/CASTE/RACEguest831335 MAGRAY-THE WARRIORS & MARTIALS OF KASHMIR:
Magray is a Martial Kashmiri tribe of Rajput origin. Magray sprung from Kashtri - un-Nassal Rajput. Kashtri-un-Nassal Rajput are one of the four classes of Hindus. Kashtri were people of ruling class having responsibility for the defence of the state. Ladhay Magray was the forefather of Magray tribe. Magres accepted Islam at the hand of Syed Ali Hamdani in thirteenth Century. The first person who entered in Kashmir and settled their belonged to Magray tribe thus making Magray tribe, the founders of Kashmir. Magray tribe ruled over Kashmir for seven hundred years. They invited Mughals to enter Kashmir in order to end disturbances in the valley. However, subsequently Mughals were defeated and pushed back by the Magray tribe. Magray tribe is settled all over the world with majority in Kashmir Valley. In spite of being SARDARS of the time, people of Magray tribe felt proud to be called as MAGRAY.
VILLAGES & TOWNS IN KASHMIR NAMED MAGRAY:
1. Magray Village District Bagh
2. Magray City Kuttan, Neelum valley, Muzaffarabad
3. Magray Hills Kanchikot, Rawalakot
4. Magray Abad, Barmang Rawalakot
5. Magray Gali Lipa Valley, Muzaffarabad
6. Magray Abad Attmaqam, Kel road,Neelum Valley
7. Magray Village Motarin, khaigala, Rawalakot
8. Kharl Magrayan District Bagh
9 Sardari Magrayan Neelum Valley Muzaffarabad
10. Magray village Marchkot Abbaspur
11. Bandian Magray Abbaspur.
MEANINGS OF MAGRAY:
Magray is an ancient word, Magray means,"TheMartials", "The Warriors", "Military and war like people". Magray is also spelled as Magrey, Magre and Magri, but the correct spelling is Magray. The plural of Magray is Magres.
BOOKS ON MAGRAY TRIBE:
All the historical books on Kashmir contain material on Magray tribe and their role in the history of Kashmir. Few of the historical Books are mentioned here for reference.
1. Magray in the Eyes of History By Sajid Latif Magray.
2. Magray A Warrior Kashmiri Tribe By Capt Ghulam Hussain Magray
3. Valley of Kashmir by Sir Walter Lirance.
4. Imperial Gazettier of India By Govt of India.
5. Tribes and Castes of Kashmir By Muhammad Din Folk.
6. Castes and Tribes of Poonch by Muhammad Din Folk.
7. History of Kasmir By Khawaja Azamey.
8. History of Kashmir By Muhammad Hassan.
9. History Kabeer Kashmir by Haji M.Mohiudin.
10. Raj Tarangi By Pandit Kahlan.
11. Tareekh-e-Kashmir by Professor Nazir Ahmed Tashna.
12. Kashmir in the Era of Muslim empires By Ghulam Hassan Khoyami.
13. Tareekh-e- Malkan By Dr Sadiq Malik.
14. Jalwa-e-Kashmir By Dr Sabir Afaqi
15. Baharistan-e-Shahi A Chronocle Mediaeval of Kashmir.
16. Magray - The Martials and Warriors of Kashmir by Sajiad Latif Magray.
17. Tareekh-e-Kashmir,Islami By Dr Sabir Afaqi.
18. Tareekh-e-Azmi By M.Azam.
19. Tribal geography of India Jamu and Kashmir By M. Bashir Magray.
20. A New History of India and Pakistan By Quyyam Abdul.
MAGRAY VILLAGES/TOWNS:
1. MAGRAY VILLAGE MOTARIN, RAWALKOT : This is a village comprising of about 400 houses, exclusively of the Magray Tribe. Road leading from Rawalkot to Tatrinote crossing point passes from this village. Few personalities of the village are: -a. Muhammad Din Magray. b. Subedar Muhammad Latif Magray. c. Sajjad Latif Magray. d. Rasheed Magray. e. Yaqoob Magray. f. Manzoor Magray. g. Sadique Magray. h. Dilpazir Magray. i. Muhammad Aamir Magray. j. Bashir Magray. k. Qayyum Magray. l. Yaseen Magray. m. Imran Yaseen marga. n. Shafi Magray. Akram Magray. p. Rafique Magray. q. Sajjad Magray. r. Muhammad Javed Magray. s. Kabir Magray. t. Kamran Magray. u.Saqib Magray
2. MAGRAY HILLS KANCHIKOT: This is a big village which starts from the Magray Market on Ali Sajal Road and goes to the Tolipeer, a prominent Hill top of Kashmir. Few personalities of the Magray Hills are:- a. Haji Aqal Hussain Magray. b. Tariq Magray. c. Ghulam Nabi Magray. d. Gulzar Magray. e. Havildar Yaseen margay. f. Sub
Chapter 4R Part II
Chapter 4R Part IIMatthew L Levy Risk and sensitivity analysis help decision makers understand how uncertain variables may impact outcomes. Sensitivity analysis describes how changes to probabilities or payoffs affect decision alternatives. This helps determine which uncertain variables need more accurate estimates. Risk analysis recognizes the difference between the expected value of decisions and possible payoffs. Sample information can provide new probabilities to update decisions. Bayes' theorem is used to compute posterior probabilities from prior probabilities and new evidence.
Create Tutorials and Videos Using SMART Recorder
Create Tutorials and Videos Using SMART Recordermr_tripp This presentation shows how to make a tutorial using SMART Record. This is a program used for SMART Interactive White Boards.
Chapter 20 Lecture Notes
Chapter 20 Lecture NotesMatthew L Levy This document summarizes key concepts in quality control and statistical process control. It discusses total quality management, the Malcolm Baldridge National Quality Award criteria, ISO 9000 standards, and Six Sigma methodology. It also describes different types of control charts used in statistical process control, including x-bar, R, p, and np charts. Control charts help determine whether process variation is due to common or assignable causes by comparing output to control limits. Interpreting point patterns on control charts indicates whether a process is in statistical control.
Loren Gray's HSMAI ROC 2016 Presentation
Loren Gray's HSMAI ROC 2016 PresentationLoren Gray, CHDM Titled, "These Are The Good Old Days" we explore the major changes that happened in 2016 that will fundamentally change how Revenue Managment for the Hospitality Industry will change.
Ad
Similar to Chapter 18 Part I (20)
Time Series Decomposition
Time Series Decompositionchandan kumar singh Time series decomposition involves breaking down a time series into various components: trend, seasonality, and error/noise. There are different decomposition models such as additive and multiplicative. Smoothing methods like moving averages are used to estimate the trend-cycle component by reducing random variation. Box-Jenkins models combine autoregressive (AR) and moving average (MA) terms to model time series, and involve identification, estimation, and diagnostic stages.
Time Series FORECASTING
Time Series FORECASTINGVarun Khandelwal This document discusses various time series forecasting methods. It begins by defining time series forecasting as making projections about future performance based on historical and current data. The goals of time series analysis are identified as identifying patterns in observed data and making forecasts. Smoothing techniques are then discussed as a way to remove random noise from time series data to better identify trends and seasonality for forecasting. Several smoothing methods are covered in detail, including simple moving averages, weighted moving averages, simple exponential smoothing, Holt's trend exponential smoothing, and Holt-Winters methods for seasonal data. The components of time series data and advantages/disadvantages of different methods are also summarized.
Lecture_03.pdf
Lecture_03.pdfjeys3 The document discusses time series data that contain deterministic trend and seasonal components in addition to irregular components. It describes how to decompose a time series into these components using either additive or multiplicative models. The key steps involve estimating the trend using a moving average, estimating the seasonal factors by averaging residuals from the trend, and identifying the irregular component as the residual.
Trend and Seasonal Components
Trend and Seasonal ComponentsAnnaRevin Tugas GSLC - Economics Statistic (Kelompok 12)
Disusun oleh :
Anna Revin Nadita
CIndy Aprilia
Prastika Ambarita
Fitri Andriyani
Demand forecasting.
Demand forecasting.ram_mulaveesala Demand forecasting involves estimating future demand for a product or service using both informal and quantitative methods. It is important for making pricing, production capacity, and market entry decisions. Methods include educated guesses, analyzing historical sales data, and using current test market data. Demand forecasting aims to minimize risks from an uncertain future by making reasonable assumptions about likely market conditions.
time series modeling.pptx
time series modeling.pptxVictoria Bozhenko Time series modeling involves describing, modeling, and predicting observations over time. Key components of time series include trends, seasonality, and irregular fluctuations. Trend models can be linear, quadratic, or log-linear. Seasonality refers to regular, predictable changes that occur each period, such as changes related to climate, holidays, or calendar effects. The purpose of seasonal adjustment is to decompose a time series into trend, seasonal, calendar, and irregular components to better understand the underlying movements in the data. Both informal graphical methods and formal statistical software can be used to detect seasonality in time series.
Enterprise_Planning_TimeSeries_And_Components
Enterprise_Planning_TimeSeries_And_Componentsnanfei This document provides an overview of time series analysis. It defines a time series as numerical data obtained at regular time intervals that occurs in many domains like economics, finance, and environment. Time series data are different from other data as they are not independent and have large sample sizes. The key components of a time series are the trend, seasonal variation, cyclical variation, and irregular/random variation. Decomposition methods are used to separate out these components. Smoothing techniques like moving averages are employed to better understand the overall patterns in time series data. Seasonal indices are calculated to measure the degree to which different seasons vary from each other.
Holtwinters terakhir lengkap
Holtwinters terakhir lengkapZulyy Astutik 1. The document discusses time series forecasting using Holt-Winters exponential smoothing methods. It focuses on analyzing seasonal time series data.
2. There are two Holt-Winters models - the multiplicative seasonal model and the additive seasonal model. The models account for trend, seasonal variations, and error in time series data.
3. Exponential smoothing assigns decreasing weights to older observations to generate forecasts. There are methods for single, double, and triple exponential smoothing to handle different patterns in time series like trend and seasonality.
timeseries_analysis.pptx a unique approach to solve the time related data
timeseries_analysis.pptx a unique approach to solve the time related dataPavanKumarMantha2 a techniques in computer science that
forecasting methods
forecasting methodsSrikavya Chowdary - Forecasting helps reduce risk and uncertainty in decision making by predicting future outcomes.
- There are three main types of forecasting methods: qualitative, extrapolative/time series, and causal/explanatory.
- Time series forecasting uses historical data patterns to predict future values, accounting for trends, seasonality, cycles, and randomness. Common time series forecasting techniques include moving averages, weighted moving averages, and exponential smoothing.
Time Series Analysis and Forecasting.ppt
Time Series Analysis and Forecasting.pptReylienCastillo1 This is a time series analysis and forecasting that is used to predict future data for sales and specified field.
Time Series Analysis and Forecasting.ppt
Time Series Analysis and Forecasting.pptssuser220491 This document discusses time series analysis and forecasting. It introduces time series data and examples. The main methods for forecasting time series are regression analysis and time series analysis (TSA), which examines past behavior to predict future behavior without causal variables. TSA involves analyzing trends, cycles, seasonality, and random variations. Forecasting accuracy is measured using techniques like mean absolute deviation and mean square error. Extrapolation models like moving averages, weighted moving averages, and exponential smoothing are discussed for forecasting, as well as approaches for stationary, additive seasonal, multiplicative seasonal, and trend data.
Chapter-3_Heizer_S1.pptx
Chapter-3_Heizer_S1.pptxEdwardDelaCruz14 Here are 3 practice problems using quantitative forecasting methods:
1. Using simple exponential smoothing, forecast next period's sales given the following data with a smoothing constant of 0.3:
Period: Sales
1: 100
2: 110
3: 120
4: ?
Forecast: F1 = 100
F2 = 100 + 0.3(110 - 100) = 103
F3 = 103 + 0.3(120 - 103) = 108.9
F4 = 108.9 + 0.3(120 - 108.9) = 113.67
2. Using linear regression, forecast next year's profits based on advertising expenditures given:
Year: Prof
forecasting
forecastingRINUSATHYAN Demand forecasting plays a key role in supply chain planning and decision making. Accurate forecasts are needed for production scheduling, inventory management, marketing activities, financial planning, and workforce management. However, forecasts are never perfectly accurate and error should be measured. Different forecasting techniques exist, including qualitative methods that use expert opinions and quantitative methods like time series analysis and regression. The bullwhip effect occurs when demand variability increases at each step up the supply chain, exacerbating distortions in information flow and potentially disrupting operations.
Forecasting
ForecastingVikash Kumar The document discusses various methods for forecasting future demand based on past demand information. It describes qualitative methods like market research and quantitative methods like time series analysis. Time series methods discussed include simple and weighted moving averages to predict the next period's demand, as well as exponential smoothing which weights recent observations more than past ones. Linear regression is also covered as a way to explore relationships between dependent and independent variables. The document emphasizes that accuracy of forecasts should be evaluated using metrics like mean absolute deviation and mean forecast error in order to compare different forecasting models.
Forecasting
ForecastingIrfan Hussain This document discusses various forecasting methods including qualitative methods like panel consensus and quantitative methods like time series analysis. It explains moving averages, weighted moving averages, and exponential smoothing for time series forecasting. Moving averages are simple to calculate but do not respond well to trends while exponential smoothing accounts for trends using smoothing constants. Linear regression can also be used to explore relationships between dependent and independent variables for forecasting. Overall the key points are that forecasting predicts future demand based on past data, different quantitative methods are suited to different situations, and accuracy depends on how well past patterns predict the future.
Forecasting linear aggression time series.ppt
Forecasting linear aggression time series.pptAbdullahZahid58 What is forecasting, example, graphs etc
Ad
Recently uploaded (20)
Linux Professional Institute LPIC-1 Exam.pdf
Linux Professional Institute LPIC-1 Exam.pdfRHCSA Guru Introduction to LPIC-1 Exam - overview, exam details, price and job opportunities
Linux Support for SMARC: How Toradex Empowers Embedded Developers
Linux Support for SMARC: How Toradex Empowers Embedded DevelopersToradex Toradex brings robust Linux support to SMARC (Smart Mobility Architecture), ensuring high performance and long-term reliability for embedded applications. Here’s how:
• Optimized Torizon OS & Yocto Support – Toradex provides Torizon OS, a Debian-based easy-to-use platform, and Yocto BSPs for customized Linux images on SMARC modules.
• Seamless Integration with i.MX 8M Plus and i.MX 95 – Toradex SMARC solutions leverage NXP’s i.MX 8 M Plus and i.MX 95 SoCs, delivering power efficiency and AI-ready performance.
• Secure and Reliable – With Secure Boot, over-the-air (OTA) updates, and LTS kernel support, Toradex ensures industrial-grade security and longevity.
• Containerized Workflows for AI & IoT – Support for Docker, ROS, and real-time Linux enables scalable AI, ML, and IoT applications.
• Strong Ecosystem & Developer Support – Toradex offers comprehensive documentation, developer tools, and dedicated support, accelerating time-to-market.
With Toradex’s Linux support for SMARC, developers get a scalable, secure, and high-performance solution for industrial, medical, and AI-driven applications.
Do you have a specific project or application in mind where you're considering SMARC? We can help with Free Compatibility Check and help you with quick time-to-market
For more information: https://www.toradex.com/computer-on-modules/smarc-arm-family
Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...
Transcript: #StandardsGoals for 2025: Standards & certification roundup - Tec...BookNet Canada Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, presentation slides, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
Drupalcamp Finland – Measuring Front-end Energy Consumption
Drupalcamp Finland – Measuring Front-end Energy ConsumptionExove How to measure web front-end energy consumption using Firefox Profiler. Presented in DrupalCamp Finland on April 25th, 2025.
tecnologias de las primeras civilizaciones.pdf
tecnologias de las primeras civilizaciones.pdffjgm517 descaripcion detallada del avance de las tecnologias en mesopotamia, egipto, roma y grecia.
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...
AI EngineHost Review: Revolutionary USA Datacenter-Based Hosting with NVIDIA ...SOFTTECHHUB I started my online journey with several hosting services before stumbling upon Ai EngineHost. At first, the idea of paying one fee and getting lifetime access seemed too good to pass up. The platform is built on reliable US-based servers, ensuring your projects run at high speeds and remain safe. Let me take you step by step through its benefits and features as I explain why this hosting solution is a perfect fit for digital entrepreneurs.
2025-05-Q4-2024-Investor-Presentation.pptx
2025-05-Q4-2024-Investor-Presentation.pptxSamuele Fogagnolo Cloudflare Q4 Financial Results Presentation
Technology Trends in 2025: AI and Big Data Analytics
Technology Trends in 2025: AI and Big Data AnalyticsInData Labs At InData Labs, we have been keeping an ear to the ground, looking out for AI-enabled digital transformation trends coming our way in 2025. Our report will provide a look into the technology landscape of the future, including:
-Artificial Intelligence Market Overview
-Strategies for AI Adoption in 2025
-Anticipated drivers of AI adoption and transformative technologies
-Benefits of AI and Big data for your business
-Tips on how to prepare your business for innovation
-AI and data privacy: Strategies for securing data privacy in AI models, etc.
Download your free copy nowand implement the key findings to improve your business.
Rusty Waters: Elevating Lakehouses Beyond Spark
Rusty Waters: Elevating Lakehouses Beyond Sparkcarlyakerly1 Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark?
At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀
TrsLabs - Fintech Product & Business Consulting
TrsLabs - Fintech Product & Business ConsultingTrs Labs Hybrid Growth Mandate Model with TrsLabs
Strategic Investments, Inorganic Growth, Business Model Pivoting are critical activities that business don't do/change everyday. In cases like this, it may benefit your business to choose a temporary external consultant.
An unbiased plan driven by clearcut deliverables, market dynamics and without the influence of your internal office equations empower business leaders to make right choices.
Getting things done within a budget within a timeframe is key to Growing Business - No matter whether you are a start-up or a big company
Talk to us & Unlock the competitive advantage
Quantum Computing Quick Research Guide by Arthur Morgan
Quantum Computing Quick Research Guide by Arthur MorganArthur Morgan This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Into The Box Conference Keynote Day 1 (ITB2025)
Into The Box Conference Keynote Day 1 (ITB2025)Ortus Solutions, Corp This is the keynote of the Into the Box conference, highlighting the release of the BoxLang JVM language, its key enhancements, and its vision for the future.
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep Dive
Designing Low-Latency Systems with Rust and ScyllaDB: An Architectural Deep DiveScyllaDB Want to learn practical tips for designing systems that can scale efficiently without compromising speed?
Join us for a workshop where we’ll address these challenges head-on and explore how to architect low-latency systems using Rust. During this free interactive workshop oriented for developers, engineers, and architects, we’ll cover how Rust’s unique language features and the Tokio async runtime enable high-performance application development.
As you explore key principles of designing low-latency systems with Rust, you will learn how to:
- Create and compile a real-world app with Rust
- Connect the application to ScyllaDB (NoSQL data store)
- Negotiate tradeoffs related to data modeling and querying
- Manage and monitor the database for consistently low latencies
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...
IEDM 2024 Tutorial2_Advances in CMOS Technologies and Future Directions for C...organizerofv IEDM 2024 Tutorial2
Generative Artificial Intelligence (GenAI) in Business
Generative Artificial Intelligence (GenAI) in BusinessDr. Tathagat Varma My talk for the Indian School of Business (ISB) Emerging Leaders Program Cohort 9. In this talk, I discussed key issues around adoption of GenAI in business - benefits, opportunities and limitations. I also discussed how my research on Theory of Cognitive Chasms helps address some of these issues
How Can I use the AI Hype in my Business Context?
How Can I use the AI Hype in my Business Context?Daniel Lehner 𝙄𝙨 𝘼𝙄 𝙟𝙪𝙨𝙩 𝙝𝙮𝙥𝙚? 𝙊𝙧 𝙞𝙨 𝙞𝙩 𝙩𝙝𝙚 𝙜𝙖𝙢𝙚 𝙘𝙝𝙖𝙣𝙜𝙚𝙧 𝙮𝙤𝙪𝙧 𝙗𝙪𝙨𝙞𝙣𝙚𝙨𝙨 𝙣𝙚𝙚𝙙𝙨?
Everyone’s talking about AI but is anyone really using it to create real value?
Most companies want to leverage AI. Few know 𝗵𝗼𝘄.
✅ What exactly should you ask to find real AI opportunities?
✅ Which AI techniques actually fit your business?
✅ Is your data even ready for AI?
If you’re not sure, you’re not alone. This is a condensed version of the slides I presented at a Linkedin webinar for Tecnovy on 28.04.2025.
Cyber Awareness overview for 2025 month of security
Cyber Awareness overview for 2025 month of securityriccardosl1 Cyber awareness training educates employees on risk associated with internet and malicious emails
Dev Dives: Automate and orchestrate your processes with UiPath Maestro
Dev Dives: Automate and orchestrate your processes with UiPath MaestroUiPathCommunity This session is designed to equip developers with the skills needed to build mission-critical, end-to-end processes that seamlessly orchestrate agents, people, and robots.
📕 Here's what you can expect:
- Modeling: Build end-to-end processes using BPMN.
- Implementing: Integrate agentic tasks, RPA, APIs, and advanced decisioning into processes.
- Operating: Control process instances with rewind, replay, pause, and stop functions.
- Monitoring: Use dashboards and embedded analytics for real-time insights into process instances.
This webinar is a must-attend for developers looking to enhance their agentic automation skills and orchestrate robust, mission-critical processes.
👨🏫 Speaker:
Andrei Vintila, Principal Product Manager @UiPath
This session streamed live on April 29, 2025, 16:00 CET.
Check out all our upcoming Dev Dives sessions at https://community.uipath.com/dev-dives-automation-developer-2025/.
Andrew Marnell: Transforming Business Strategy Through Data-Driven Insights
Andrew Marnell: Transforming Business Strategy Through Data-Driven InsightsAndrew Marnell With expertise in data architecture, performance tracking, and revenue forecasting, Andrew Marnell plays a vital role in aligning business strategies with data insights. Andrew Marnell’s ability to lead cross-functional teams ensures businesses achieve sustainable growth and operational excellence.
Chapter 18 Part I
- 1. Chapter 18 Part I Matt Levy - ISDS 2001
- 2. Forecasting An important way that we can use models is to plan for the future. This is obviously important from a work perspective, as our ability to model what is going to happen in the future, and it be relatively accurate, is important We must remember the process to do this is quite difficult as we have to deal with historical data over time. This historical data on a single variable is called time series . The procedure to do forecasting is called time series methods. Forecasting methods can be quantitative or qualitative. For the purposes of this course we are obviously interested in quantitative. The quantitative methods we will look at can be studied when: (1) past information about the variable being forecast is available. (2) the information can be quantified. (3) there is a reasonable assumption that the patterns in the past will predict the future
- 3. Components of a Time Series The gradual shifting of a time series is referred to as a trend . Trends can be linear, nonlinear, or simply have no trend at all. Below is an overview of all the different forecasting methods that will soon be available to you:
- 4. Cyclical Component Trend lines will tend to show groups of points that cycle above and below the trend line. Any recurring sequence of point above or below the trend line lasting more than a year can be attributed to the cyclical component of the time series. For example: A boom or bust cycle in the economy will cause market indices to be below normal for an elongated period of time. Shown below, the S&P 500 2000-2009. Trends can also have seasonal, or simply irregular components.
- 5. Smoothing Methods Moving averages, weighted averages, and exponential smoothing are three types of smoothing methods. The objective of each of the methods is to smooth out random fluctuations caused by irregular components of the time series. Smoothing methods are appropriate for stable time series -That is, ones that do not exhibit significant cyclical effects such as seasonal. They do not work well when these types of cyclical effects are present. In particular exponential smoothing is good for large numbers of items.
- 6. Moving Averages Uses the most recent n data points in the time series as the forecast for the next period. Moving Average = Σ(most recent data values)/n The term moving is used because every time a new data point becomes available is replaces the oldest one.
- 7. Weighted Moving Average Involves finding a different weight for each data value and multiplying each point by that value in the moving average calculation. Obviously, this is useful for when we are not dealing with too many data points. Weighted Average = (w1*p1 + w2*p2 + ...+ wn*pn)/n In practice, forecast Accuracy is a big issue here as assigning the values to use and weights to them requires in-depth knowledge of what you are trying to forecast.
- 8. Exponential Smoothing Uses a weighted average of past time series values as the forecast. Special case of the weighted moving average as we select only one weight--the weight for the most recent observation. The weights for the other values are computed automatically and become smaller as the observations move farther in to the past. Ft+1 = αYt + (1 - α)Ft where: Ft+1 = forecast for the time series period for t+1. Yt = actual value of the time series in period t Ft = forecast of the time series for period t. α = smoothing constant (0 ≤ α ≤ 1).
- 9. Trend Projection Recall, in Simple Linear Regression: ŷ = b0 + b1x In forecasting the independent variable is time. To emphasize this we re-write the equation above as follows: Tt = b0 + b1t, where Tt = trend value of the time series in period t. b0 = intercept of the trend line. b1 = slope of the trend line. t = time (lower t's correspond to the oldest time series value) Also similar to SLR is how we compute b0 and b1. For these equations, refer to Figures 18.6 and 18.7 in the book.
- 10. End of Chapter 18 Part I Let me know if you have any questions. Please read the chapter. Please do your homework.