Overview:
Time Series performs univariate and multivariate analysis and enables you to explore both stationary and nonstationary models. You can select a model to fit your data and obtain estimates of the model's parameters. Choose from standard methods such as Yule-Walker, Levinson-Durbin, long autoregression, Hannan-Rissanen, and others.
After reading in and plotting your data, use the built-in Time Series transforms for linear filtering, simple exponential smoothing, differencing, moving averages, and more to transform your raw data into a form suitable for modeling. Calculating and plotting the correlation and partial correlation functions will help you spot patterns. Once you select a model to fit your data, Time Series makes it easy to estimate the model parameters and check its validity using residuals and tests such as the portmanteau, turning points, difference-sign, and others.
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