**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 â€“ ARIMA(p,d,q) | |||

2.3k ARIMA Ordering â€“ 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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