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Time Series

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Note: This is a discontinued product. Archival documentation »

The Mathematica application pacakge Time Series is a fully integrated environment providing time-dependent data analysis and data analysis functionality. Using Time Series, you can do the following:
  • Perform univariate and multivariate analysis
  • Explore both stationary and nonstationary models
  • Use standard parameter estimation methods such as Yule-Walker, Levinson-Durbin, maximum likelihood, and long autoregression
  • Check diagnostics using such tests as portmanteau, turning points, and difference-sign
  • Forecast future values using exact and approximate best linear predictors
  • Analyze your data in frequency space using spectral analysis tools

*Mathematics > Probability and Statistics
*Wolfram Technology > Application Packages > Wolfram Research Applications > Time Series

linear time series analysis, data smoothing, forecasting, autoregressive moving average models, spectral analysis, multivariate time series models, parameter estimation, diagnostic checking, time dependent data analysis, large sample approximation, conditional maximum likelihood method, data transformation, seasonal ARIMA process, SARIMA, ARCH models, GARCH models, fourier transforms, Kalman filter, state-space, covariance, correlation, univariate, multivariate models, linear filtering, ARMA models, approximate best linear predictor, turning points test, difference sign test, portmanteau test, hannan rissanen procedure, yule walker method, levinson durbin algorithm, long autoregression, differencing, times series software