Spotfire Statistica® Time Series Forecasting

The Time Series / Forecasting module contains techniques that are useful for analyzing time series data, that is, sequences of measurements that follow non-random orders. Unlike the analyses of random samples of observations that are discussed in the context of most other statistics, the analysis of time series is based on the assumption that successive values in the data file represent consecutive measurements taken at equally spaced time intervals.

Detailed discussions of the methods can be found in Anderson (1976), Box and Jenkins (1976), Kendall (1984), Kendall and Ord (1990), Montgomery, Johnson, and Gardiner (1990), Pankratz (1983), Shumway (1988), Vandaele (1983), Walker (1991), and Wei (1989).

Common smoothing options are available to "bring out" the major patterns; weighted, prior, n-points moving average, simple exponential, 4253 filters, etc. 

Types of analyses are single series ARIMA, exponential smoothing, interrupted ARIMA, Fourier, Census I (seasonal decomposition), Census II (x-11 seasonal), and distributed lag.

Time Series problems can also be analyzed with neural networks. Neural networks are not included in Spotfire Statistica® Desktop or Spotfire Statistica® Analyst. You must purchase Spotfire Statistica® Modeler, Spotfire Statistica® Data Scientist, or Spotfire Statistica® Comprehensive to receive neural networks.