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STSA - The Statistical Time Series Analysis Toolbox for O-Matrix Version Enhancements and Additions Summary

Scatterplot with Non-Parametric Fit

The STSA (Statistical Time Series Analysis) Toolbox Version 2.1 release includes a large number of enhancements and additional functions. Some of the new areas of functionality include:

  • There are 2 new sub-directories, or functional categories that expand the existing capabilities of STSA: POD; Proper Orthogonal Decomposition and Singular Spectrum Analysis, and NONPAR; functions for nonparametric, nonlinear time series analysis.
  • All sub-directories have been updated, enhanced, and expanded with new functions - STSA can now handle a greater array of time series problems.
  • More functions have been added that can be used and in non-time series contexts: more random number generators, cumulative distribution functions, probability density functions, statistical tools, and generic optimization.
Some specific version 2.1 enhancements include:
  • Functions for performing singular spectrum analysis (SSA) of a time series including decomposition, reconstruction and forecasting.
  • Functions for handling nonlinear time series using nonparametric models, including local polynomials, cubic splines, functional coefficient models, partially linear models and various cross validation methods for automated bandwidth selection.
  • Enhanced statistical tools (logistic regression for handling binary time series, enhanced QQ plot function).
  • More examples that illustrate and expand on existing and new functional capabilities.
  • New samples using provide real-world, and simulated data sets.

The STSA Toolbox Version 2.0 release included a large number of enhancements and additional functionality including:

  • Four new sub-directories (FILTER, OPTIMIZE, RNG & STATS) that expand the capabilities of STSA.
  • All sub-directories have been updated and expanded with new functions - STSA can now handle a greater array of time series problems.
  • STSA now contains additional functions that can be used and in non-time series contexts (random numbers, statistical tools, generic optimization).
  • Many functions now contain formatted screen output that greatly enhances the speed and quality of any analysis.
Some specific version 2.0 enhancements included:
  • Compute the theoretical autocovariances of an ARMA model.
  • Durbin-Levinson-Whittle algorithm for computing innovations.
  • Perform Granger causality tests.
  • Compare forecasting performance of competing models.
  • Compute robust estimates using Least Absolute Deviations.
  • Filter a time series using a variety of filtering methods and models (Savitzky- Golay, generic finite impulse response, time-invariant Kalman filter with estimation, Holt-Winters with seasonal and estimation).
  • Estimate the empirical probability density and cumulative density functions.
  • Bootstrap a time series using the maximum entropy bootstrap.
  • Estimate ARMA-GARCH models.
  • Enhanced nonlinear optimization functions with optional screen output.
  • Random numbers from various statistical distributions.
  • Enhanced frequency domain functions (additional methods for estimating the spectrum, estimation of cross-spectrum, squared coherence, amplitude and phase, additional methods for long-memory models, enhanced plots).
  • Enhanced statistical tools (regression with full screen output, enhanced plots, transformations and tests for Gaussianity, PCA and Factor Analysis).

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