6. Process or Product Monitoring and Control

6.4.	Introduction to Time Series Analysis

Time series methods take into account possible internal structure in the data

Time series data often arise when monitoring industrial processes or tracking corporate business metrics. The essential difference between modeling data via time series methods or using the process monitoring methods discussed earlier in this chapter is the following:

Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for.

This section will give a brief overview of some of the more widely used techniques in the rich and rapidly growing field of time series modeling and analysis.

Contents for this section

Areas covered are:

1. Definitions, Applications and Techniques
2. What are Moving Average or Smoothing Techniques?
1. Single Moving Average
2. Centered Moving Average
3. What is Exponential Smoothing?
1. Single Exponential Smoothing
2. Forecasting with Single Exponential   Smoothing
3. Double Exponential Smoothing
4. Forecasting with Double Exponential Smoothing
5. Triple Exponential Smoothing
6. Example of Triple Exponential Smoothing
7. Exponential Smoothing Summary
4. Univariate Time Series Models
1. Sample Data Sets
2. Stationarity
3. Seasonality
4. Common Approaches
5. Box-Jenkins Approach
6. Box-Jenkins Model Identification
7. Box-Jenkins Model Estimation
8. Box-Jenkins Model Validation
9. Example of Univariate Box-Jenkins Analysis
10. Box-Jenkins Model Analysis on Seasonal Data
5. Multivariate Time Series Models
1. Example of Multivariate Time Series Analysis