ITSM for Windows: A User’s Guide to Time Series Modelling and Forecasting
Springer Science & Business Media, Aug 12, 1994 - Computers - 118 pages
The analysis of time series data is an important aspect of data analysis across a wide range of disciplines, including statistics, mathematics, business, engineering, and the natural and social sciences. This package provides both an introduction to time series analysis and an easy-to-use version of a well-known time series computing package called Interactive Time Series Modelling. The programs in the package are intended as a supplement to the text Time Series: Theory and Methods, 2nd edition, also by Peter J. Brockwell and Richard A. Davis. Many researchers and professionals will appreciate this straightforward approach enabling them to run desk-top analyses of their time series data. Amongst the many facilities available are tools for: ARIMA modelling, smoothing, spectral estimation, multivariate autoregressive modelling, transfer-function modelling, forecasting, and long-memory modelling. This version is designed to run under Microsoft Windows 3.1 or later. It comes with two diskettes: one suitable for less powerful machines (IBM PC 286 or later with 540K available RAM and 1.1 MB of hard disk space) and one for more powerful machines (IBM PC 386 or later with 8MB of RAM and 2.6 MB of hard disk space available).
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ACF and PACF ACF/PACF ACVF AICC statistic AICC value AIRPASS.MOD ARMA model ARMA process ARVEC ASCII autoregressive BD Section bivariate Box-Cox transformation BURG choose coefficients compute cross correlations current model data file Data Menu data set differencing at lag differencing operations displayed double click enter Estimation and Prediction Estimation Menu EXAMPLE exit exponential smoothing fitted model forecasts function key Gaussian likelihood icon input ITSM itsmw window key to continue LEAD.DAT LONGMEM LS2.DAT Main Menu mean squared errors memory-shortening minimum AICC moving average multivariate observations optimization periodogram PEST plot Prediction Menu predictors Press any key Results Menu run the program SALES.DAT sample ACF sample mean select the option series Xt shown in Figure smoothed values specified Spectral Analysis Menu spectral density standard error stationary process stored in PEST transfer function model transformed series Vertical scale weight function white noise sequence white noise variance WORD6 zero