# Statistics - Advanced Linear/Nonlinear Models - Time Series/Forecasting

Ribbon bar. Select the Statistics tab. In the Advanced/Multivariate group, click Advanced Models and on the menu, select Time series/Forecasting to display the Time Series Analysis Startup Panel.

Classic menus. On the Statistics - Advanced Linear/Nonlinear Models submenu, select Time Series/Forecasting to display the Time Series Analysis Startup Panel.

A time series is a sequence of measurements, typically taken at successive points in time. Time series analysis includes a broad spectrum of exploratory and hypothesis testing methods that have two main goals: (a) identifying the nature of the phenomenon represented by the sequence of observations, and (b) forecasting (predicting future values of the time series variable). Both of these goals require that the pattern of observed time series data is identified and more or less formally described. Once the pattern is established, you can interpret and integrate it with other data (i.e., use it in our theory of the investigated phenomenon, e.g., seasonal commodity prices).

The Time Series module contains a wide range of descriptive, modeling, decomposition, and forecasting methods for both time and frequency domain models. These procedures are integrated, that is, the results of one analysis (e.g., ARIMA residuals) can be used directly in subsequent analysis (e.g., to compute the autocorrelation of the residuals). Various methods for transforming and smoothing the time series (prior to an analysis) are supported, including: de-trending, removal of autocorrelation, moving average smoothing (unweighted and weighted, with user-defined or Daniell, Tukey, Hamming, Parzen, or Bartlett weights), moving median smoothing, simple exponential smoothing (see also the description of all exponential smoothing options below), differencing, integrating, residualizing, shifting, 4253H smoothing, tapering, Fourier (and inverse) transformations, and others. Autocorrelation, partial autocorrelation, and crosscorrelation analyses can also be computed. Specialized time-series fitting and modeling procedures include ARIMA and interrupted time series (intervention) analysis, a complete implementation of all 12 common exponential smoothing models (with or without trend and seasonal components), classical seasonal decomposition (Census Method I), X-11 Monthly and Quarterly Seasonal Decomposition and Seasonal Adjustment (Census Method II), Polynomial Distributed Lag Models, Spectrum (Fourier) and Cross-Spectrum Analysis, etc. STATISTICA also includes numerous options for plotting the data, autocorrelations and cross-correlations, components, etc.