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).

Designed for the analysis of linear time series and the practical modelling and prediction of data collected sequentially in time. It provides the reader with a practical understanding of the six programs contained in the ITSM software (PEST, SPEC, SMOOTH, TRANS, ARVEC, and ARAR). This IBM compatible software is included in the back of the book on two 5 1/4'' diskettes and on one 3 1/2 '' diskette. - Easy to use menu system - Accessible to those with little or no previous compu- tational experience - Valuable to students in statistics, mathematics, busi- ness, engineering, and the natural and social sciences. This package is intended as a supplement to the text by the same authors, "Time Series: Theory and Methods." It can also be used in conjunction with most undergraduate and graduate texts on time series analysis.