Methods for analyzing time series

Minitab offers several analyses that let you to analyze time series. The simple forecasting and smoothing methods model components in a series that is usually easy to observe in a time series plot of the data. This approach decomposes the data into its component parts, and then extends the estimates of the components into the future to provide forecasts. You can choose the static method of trend analysis or the dynamic methods of moving average, single and double exponential smoothing. Static methods have patterns that do not change over time; dynamic methods have patterns that do change over time and estimates are updated using neighboring values.

Trend Analysis

Fits a general trend model to time series data. Choose between the linear, quadratic, exponential growth or decay, and S-curve trend models. Use this procedure to fit trend when there is no seasonal component in your series.

Forecasts:
  • Length: long
  • Profile: extension of trend line

Moving Average

Smoothes your data by averaging consecutive observations in a series. You can use this procedure when your data do not have a trend component. If you have a seasonal component, set the length of the moving average to equal the length of the seasonal cycle.

Forecasts:
  • Length: short
  • Profile: flat line

Single Exponential Smoothing

Smoothes your data using the optimal one-step ahead ARIMA (0,1,1) forecasting formula. This procedure works best without a trend or seasonal component. The single dynamic component in a moving average model is the level.

Forecasts:
  • Length: short
  • Profile: flat line

Double Exponential Smoothing

Smoothes your data using the optimal one-step-ahead ARIMA (0,2,2) forecasting formula. This procedure can work well when there is a trend but it can also serve as a general smoothing method. Double Exponential Smoothing calculates dynamic estimates for two components: level and trend.

Forecasts:
  • Length: short
  • Profile: straight line with slope equal to last trend estimate
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