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Course Overview

 

Welcome to Time Series Modeling Essentials. I’m Mark Huber. Since 2004, I’ve been an Analytic Training Consultant in SAS Education, where I’ve taught courses covering ANOVA, regression, logistic regression, survival analysis, cluster analysis, and other analytical methods using SAS software. I’ve also costarred in a video series about statistics on YouTube called Stat Wars.

This course guides you through three commonly used modeling families for analyzing time series data. If you’re familiar with the fundamental statistical and analytic methods of ANOVA and regression and are interested in adding the time dimension to your analytical toolkit, you should do very well in this course.

Now suppose you’re in charge of a call center and you have to make staffing decisions. You don’t want to over-staff, wasting money and effort, but neither do you want to understaff, putting your customers on hold too long, potentially irritating them and losing their business.

What if you’re a farmer trying to determine how much you should budget for water based on the amount of rainfall that’s likely this year? Time series forecasting methods using information obtained about call volume or rainfall patterns in the past can help you make better decisions about the future.

In addition to the introduction to the analytical techniques, this course also describes the use of SAS Studio, which is a user interface to SAS and includes point and click tasks for all of the methods in this course. The tasks make learning the SAS programming syntax quick and easy.

We’d love to hear from you, so please take advantage of the feedback form which is linked to this course. Now let’s get started.

Course Curriculum

1.1a Introduction
1.1a Introduction 00:00:00
1.1b Objectives
1.1b Objectives 00:00:00
1.2a Time Series
1.2a Time Series 00:00:00
1.2bTransactional Data Example
1.2bTransactional Data Example 00:00:00
1.2c Transactional Data Time Binning Accumulate Total
1.2c Transactional Data Time Binning Accumulate Total 00:00:00
1.2d Transactional Data Time Binning Accumulate Average
1.2d Transactional Data Time Binning Accumulate Average 00:00:00
1.2e Transactional Data Time Binning Accumulate Maximum
1.2e Transactional Data Time Binning Accumulate Maximum 00:00:00
1.2f Transactional Data Time Binning Comparing Accumulation Methods
1.2f Transactional Data Time Binning Comparing Accumulation Methods 00:00:00
1.2g Demo Time Series Creation
1.2g Demo Time Series Creation 00:00:00
1.2h Exercise Creating a New Time Series Exploration Task Question
1.2h Exercise Creating a New Time Series Exploration Task Question 00:00:00
1.2h Exercise Creating a New Time Series Exploration Task Answer
1.2h Exercise Creating a New Time Series Exploration Task Answer 00:00:00
1.3a Variation in Time Series Data Two Main Parts
1.3a Variation in Time Series Data Two Main Parts 00:00:00
1.3b The Airline Data
1.3b The Airline Data 00:00:00
1.3c Demo Time Series Identification
1.3c Demo Time Series Identification 00:00:00
1.3d Exercise Using the TIMESERIES Procedure and Creating Appropriate Decomposition Plots Exercise
1.3d Exercise Using the TIMESERIES Procedure and Creating Appropriate Decomposition Plots Exercise 00:00:00
1.3d Exercise Using the TIMESERIES Procedure and Creating Appropriate Decomposition Plots Answer
1.3d Exercise Using the TIMESERIES Procedure and Creating Appropriate Decomposition Plots Answer 00:00:00
1.4a Necessary Conditions for Good Forecasts
1.4a Necessary Conditions for Good Forecasts 00:00:00
1.4b Time Series Models in This Course
1.4b Time Series Models in This Course 00:00:00
1.4c Exponential Smoothing (ESM) Model
1.4c Exponential Smoothing (ESM) Model 00:00:00
1.4d Box-Jenkins ARIMAX Models
1.4d Box-Jenkins ARIMAX Models 00:00:00
1.4e Unobserved Components Models (UCMs)
1.4e Unobserved Components Models (UCMs) 00:00:00
1.4f Choosing the Right Model for the Job
1.4f Choosing the Right Model for the Job 00:00:00
1.5a SAS Studio Overview
1.5a SAS Studio Overview 00:00:00
2.1a ARIMAX Models Introduction
2.1a ARIMAX Models Introduction 00:00:00
2.1b ARIMAX Models Objective
2.1b ARIMAX Models Objective 00:00:00
2.2a Forecasting
2.2a Forecasting 00:00:00
2.2b Demo Predictability of Dice Rolls
2.2b Demo Predictability of Dice Rolls 00:00:00
2.2c Autocorrelation
2.2c Autocorrelation 00:00:00
2.2d White Noise
2.2d White Noise 00:00:00
2.2e The Ljung-Box Chi-Square Test for White Noise
2.2e The Ljung-Box Chi-Square Test for White Noise 00:00:00
2.2f Forecasting Solar Power Production
2.2f Forecasting Solar Power Production 00:00:00
2.2g Autocorrelation and Solar Production
2.2g Autocorrelation and Solar Production 00:00:00
2.3a What Is ARIMA
2.3a What Is ARIMA 00:00:00
2.3b Stationarity
2.3b Stationarity 00:00:00
2.3c ARMA and Stationarity
2.3c ARMA and Stationarity 00:00:00
2.3d First Differencing Example
2.3d First Differencing Example 00:00:00
2.3e ARMA versus ARIMA Models
2.3e ARMA versus ARIMA Models 00:00:00
2.3f Regression of Y on Past Y Autoregression
2.3f Regression of Y on Past Y Autoregression 00:00:00
2.3g Determining Autoregressive Order
2.3g Determining Autoregressive Order 00:00:00
2.3h Partial Autocorrelation Function Plot (PACF)
2.3h Partial Autocorrelation Function Plot (PACF) 00:00:00
2.3i Autoregressive verses Moving Average Models
2.3i Autoregressive verses Moving Average Models 00:00:00
2.3j Moving Average
2.3j Moving Average 00:00:00
2.3k ARIMA Ordering – ARIMA(p,d,q)
2.3k ARIMA Ordering – ARIMA(p,d,q) 00:00:00
2.3l Demo Time Series Identification
2.3l Demo Time Series Identification 00:00:00
2.3m Exercise Analyzing a Rose Sale Series
2.3m Exercise Analyzing a Rose Sale Series 00:00:00
2.3m Exercise Analyzing a Rose Sale Series Answer
2.3m Exercise Analyzing a Rose Sale Series Answer 00:00:00
2.4a Forecasting Using Statistical Models
2.4a Forecasting Using Statistical Models 00:00:00
2.4b Estimation of an AR(1) Model
2.4b Estimation of an AR(1) Model 00:00:00
2.4c Accuracy versus Goodness of Fit
2.4c Accuracy versus Goodness of Fit 00:00:00
2.4d Model Goodness-of-Fit Statistics
2.4d Model Goodness-of-Fit Statistics 00:00:00
2.4e Check of Residuals
2.4e Check of Residuals 00:00:00
2.4f Demo Estimation, Residual Analysis, and Goodness-of-Fit
2.4f Demo Estimation, Residual Analysis, and Goodness-of-Fit 00:00:00
2.4g Exercise Rose Series Estimation Exercise
2.4g Exercise Rose Series Estimation Exercise 00:00:00
2.4g Exercise Rose Series Estimation Solution
2.4g Exercise Rose Series Estimation Solution 00:00:00
2.5a Linear Regression Analogy
2.5a Linear Regression Analogy 00:00:00
2.5b Time Series Regression Terminology
2.5b Time Series Regression Terminology 00:00:00
2.5c Types of Regressors in Time Series Models
2.5c Types of Regressors in Time Series Models 00:00:00
2.5e The Cross-Correlation Function (CCF)
2.5e The Cross-Correlation Function (CCF) 00:00:00
2.5f Spurious Correlation
2.5f Spurious Correlation 00:00:00
2.5g Demo Cloud Cover and Solar Power
2.5g Demo Cloud Cover and Solar Power 00:00:00
2.5h Demo Estimation of Cloud Cover
2.5h Demo Estimation of Cloud Cover 00:00:00
2.5i Events and Intervention Analysis
2.5i Events and Intervention Analysis 00:00:00
2.5j Primary Event Variables
2.5j Primary Event Variables 00:00:00
2.5k Example Rose Sales
2.5k Example Rose Sales 00:00:00
2.5l Exercise Intervention Analysis of the Rose Series Exercise
2.5l Exercise Intervention Analysis of the Rose Series Exercise 00:00:00
2.5l Exercise Intervention Analysis of the Rose Series Solution
2.5l Exercise Intervention Analysis of the Rose Series Solution 00:00:00
2.6a Forecasting and Liability
2.6a Forecasting and Liability 00:00:00
2.6b Honest Assessment Simulating a Retrospective Study
2.6b Honest Assessment Simulating a Retrospective Study 00:00:00
2.6c Model Fit Statistics
2.6c Model Fit Statistics 00:00:00
2.6d The Stochastic Input Variable Conundrum
2.6d The Stochastic Input Variable Conundrum 00:00:00
2.6e Demo Forecasting a Holdout Sample Using the ARIMA Model
2.6e Demo Forecasting a Holdout Sample Using the ARIMA Model 00:00:00
2.6f Demo Forecasting a Holdout Sample Using the ARIMAX Model
2.6f Demo Forecasting a Holdout Sample Using the ARIMAX Model 00:00:00
2.6g Demo Comparing Models Using MAPE
2.6g Demo Comparing Models Using MAPE 00:00:00
2.6h Demo Forecasting Future Values Using the Champion Model
2.6h Demo Forecasting Future Values Using the Champion Model 00:00:00
2.6i Exercise Validation and Forecasting of Rose Series 4 Exercise
2.6i Exercise Validation and Forecasting of Rose Series 4 Exercise 00:00:00
2.6i Exercise Validation and Forecasting of Rose Series 4 Exercise Solution
2.6i Exercise Validation and Forecasting of Rose Series 4 Exercise Solution 00:00:00
3.0 Exponential Smoothing Models Introduction
3.0 Exponential Smoothing Models Introduction 00:00:00
3.0 Exponential Smoothing Models Objectives
3.0 Exponential Smoothing Models Objectives 00:00:00
3.1a Weighted Average Example Random Walk
3.1a Weighted Average Example Random Walk 00:00:00
3.1b Weighted Average Example- Simple Moving Average
3.1b Weighted Average Example- Simple Moving Average 00:00:00
3.1c Weighted Average Example- Weighted Moving Average
3.1c Weighted Average Example- Weighted Moving Average 00:00:00
3.1d Exponential Smoothing Models
3.1d Exponential Smoothing Models 00:00:00
3.1e Exponential Smoothing Models (ESM)
3.1e Exponential Smoothing Models (ESM) 00:00:00
3.1f ODS Graphics and ESM ODS Output
3.1f ODS Graphics and ESM ODS Output 00:00:00
3.1g PROC ESM Syntax and the SAS Studio Task
3.1g PROC ESM Syntax and the SAS Studio Task 00:00:00
3.1h Sea Surface Temperatures (SST)
3.1h Sea Surface Temperatures (SST) 00:00:00
3.1i Demo- Analyzing Sea Surface Temperatures Using SAS Studio
3.1i Demo- Analyzing Sea Surface Temperatures Using SAS Studio 00:00:00
3.1j Demo- Analyzing Sea Surface Temperatures with an Exponential Smoothing Model
3.1j Demo- Analyzing Sea Surface Temperatures with an Exponential Smoothing Model 00:00:00
3.1l Exercise- Determining a Trend or Seasonal Component
3.1l Exercise- Determining a Trend or Seasonal Component 00:00:00
3.1l Solution- Determining a Trend or Seasonal Component
3.1l Solution- Determining a Trend or Seasonal Component 00:00:00
4.0 Unobserved Components Models Introduction
4.0 Unobserved Components Models Introduction 00:00:00
4.0 Unobserved Components Models Objectives
4.0 Unobserved Components Models Objectives 00:00:00
4.1a Time Series Analysis- UCM Goals
4.1a Time Series Analysis- UCM Goals 00:00:00
4.1b Airline Data Example
4.1b Airline Data Example 00:00:00
4.1c Demo- Creating the Unit Series on a Monthly Interval Using an Average Accumulation Method
4.1c Demo- Creating the Unit Series on a Monthly Interval Using an Average Accumulation Method 00:00:00
4.1d Demo- Specifying an Unobserved Components Model
4.1d Demo- Specifying an Unobserved Components Model 00:00:00
4.2a The Nature of Components
4.2a The Nature of Components 00:00:00
4.2b Trend Component Example
4.2b Trend Component Example 00:00:00
4.2c Season Component Example
4.2c Season Component Example 00:00:00
4.2d A General UCM
4.2d A General UCM 00:00:00
4.2e A General Model Building Approach
4.2e A General Model Building Approach 00:00:00
4.2g Demo- Refining an Unobserved Components Model
4.2g Demo- Refining an Unobserved Components Model 00:00:00
Course Notes: Time Series Modeling Essentials File
Course Notes: Time Series Modeling Essentials File 00:00:00

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