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.
Select the target field: Select the field from the data stream for which you wish to create a time series plot. Measurements for this field need to be made at regular time intervals (e.g., daily, monthly, quarterly, etc.).
Target field frequency: Choose the time interval for the observations of the target field.
Series starting period (optional)...: This option allows the user to specify the starting period of the time series, which will be reflected in the forecast plot.
Plot type: Select the plot type.
Graph resolution: Select the resolution of the graph in dots per inch: 1x (96 dpi); 2x (192 dpi); or 3x (288 dpi). Lower resolution creates a smaller file and is best for viewing on a monitor. Higher resolution creates a larger file with better print quality.
An Alteryx R-Graph object that can be used to assist in the creation of custom reports.
*Hyndman, R.J. and Athanasopoulos, G. (2012) Forecasting: Principles and Practice.
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