Which time series analyses are included in Minitab?

Minitab offers several simple forecasting and smoothing methods, correlation analysis methods, and ARIMA modeling techniques to analyze your time series data.
Time series plot
To plot the data in time order to determine whether there is a trend or seasonal pattern, create a time series plot. In Minitab, choose Stat > Time Series > Time Series Plot.
Trend analysis
To fit trend lines using a linear, quadratic, growth, or S curve trend model, perform a trend analysis. In Minitab, choose Stat > Time Series > Trend Analysis.
Decomposition
To fit a model that weights all observations equally to determine the best regression fit, perform a decomposition analysis. Use when your series exhibits a seasonal pattern, with or without a trend. In Minitab, choose Stat > Time Series > Decomposition.
Moving average
To smooth your series using a method that averages recent observations and excludes older observations, use a moving average method. Do not use when your series exhibits a trend. In Minitab, choose Stat > Time Series > Moving Average.
Single exponential smoothing
To smooth your series using a method that gives decreasing weights to older observations when your time series does not exhibit a trend or a seasonal pattern, use a single exponential smoothing method. In Minitab, choose Stat > Time Series > Single Exp Smoothing.
Double exponential smoothing
To smooth your series using a method that gives decreasing weights to older observations when your time series exhibits a trend but not a seasonal pattern, use a double exponential smoothing method. In Minitab, choose Stat > Time Series > Double Exp Smoothing.
Winters' method
To smooth your series using a method that gives decreasing weights to older observations when your time series exhibits a seasonal pattern, with or without a trend, use the Winters' smoothing method. In Minitab, choose Stat > Time Series > Winters’ Method.
Differences
Create a new column of data for custom analyses and plots and store the differences between observations within a series. In Minitab, choose Stat > Time Series > Differences.
Lag
Create a new column of data for custom analyses and plots and shift a series down by a specific number of rows in the worksheet. In Minitab, choose Stat > Time Series > Lag.
Autocorrelation
To measure how well observations at different points time correlate with each other and look for a seasonal pattern, perform an autocorrelation analysis. Use this analysis in conjunction with the Partial autocorrelation function to identify the components for an ARIMA model. In Minitab, choose Stat > Time Series > Autocorrelation.
Partial autocorrelation
To measure how well past observations in a time series correlate with future observations, while accounting for observations that are between the correlation pair, perform a partial autocorrelation analysis. Use this analysis in conjunction with the Autocorrelation function to identify the components for an ARIMA model. In Minitab, choose Stat > Time Series > Partial Autocorrelation.
Cross correlation
To determine whether one series predicts another by plotting the correlations between the two series at different points in time, perform a cross correlation analysis. In Minitab, choose Stat > Time Series > Cross Correlation.
ARIMA
To fit a model with autoregressive, difference, and moving average components, perform an ARIMA. To fit an ARIMA model, you must understand the autocorrelation and partial autocorrelation structure of your series. In Minitab, choose Stat > Time Series > ARIMA.
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