TS Plot Tool

The TS Plot tool provides a number of different univariate time series plots that are useful in both better understanding the time series data and determining how to proceed in developing a forecasting model.

The available plots are a basic time series plot to help assess whether the original time series needs to be transformed and whether there are outliers in the series; a seasonal plot that allows for an assessment of the existence and nature of any seasonality in the series; a seasonal deviation plot that allows for an assessment of whether the underlying nature of the time series varies across seasons; autoregression function and partial autoregression function plots to help determine the nature of any underlying autocorrelation and in assessing the possible needs in terms of data differencing for the creation of an ARIMA model; and a time series decomposition plot that allows for a visual examination of the original data, the trend in the data, the seasonality in the data, and information about the residuals once seasonality and trend have been take into account. The time series decomposition plot is based on using the non-parametric regression (loess) R function stl().

A more detailed description of the plots and methods provided by this tool can be found in Chapters 2 and 6 of Hyndman and Athanasopoulos's online book Forecasting: Principals and Practice.*

This tool uses the R programming language. Go to Options > Download Predictive Tools to install R and the packages used by the R Tool.

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Graphics Options

Outputs

*Hyndman, R.J. and Athanasopoulos, G. (2012) Forecasting: Principles and Practice.