000 02152nam a22002417a 4500
999 _c2054
_d2054
005 20220316171608.0
008 220316b ||||| |||| 00| 0 eng d
020 _a9789390219452
082 _a338.5442
_bKET
100 _aKeating, Barry
_94961
245 _aForecasting and predictive analytics: with ForecastX
250 _a7th
260 _bMcGraw Hill Education (India) Pvt. Ltd.
_aChennai
_c2022
300 _axviii, 539 p.
365 _aINR
_b725.00
504 _aChapter 1 Introduction to Business Forecasting and Predictive Analytics Chapter 2 The Forecast Process, Data Considerations, and Model Selection Chapter 3 Extrapolation 1. Moving Averages and Exponential Smoothing Chapter 4 Extrapolation 2. Introduction to Forecasting with Regression Trend Models Chapter 5 Explanatory Models 1. Forecasting with Multiple Regression Causal Models Chapter 6 Explanatory Models 2. Time-Series Decomposition Chapter 7 Explanatory Models 3. ARIMA (Box-Jenkins) Forecasting Models Chapter 8 Predictive Analytics: Helping to Make Sense of Big Data Chapter 9 Classification Models: The Most Used Models in Analytics Chapter 10 Ensemble Models and Clustering Chapter 11 Text Mining Chapter 12 Forecast/Analytics Implementation
520 _aOVERVIEW The seventh edition of Forecasting and Predictive Analytics with ForecastX™ builds on the success of the previous editions. While a number of significant changes have been made in this edition, it remains a book about prediction methods for managers, forecasting practitioners, data scientists, and students aspiring to become business professionals and have a need to understand practical issues related to prediction in all its forms. The text is designed to lead through the most helpful techniques in any prediction effort. Most of the examples in the book are based on actual historical data and the techniques are explained as procedures that users may replicate with their own data.
650 _a Business forecasting
_96021
700 _aWilson, J. Holton
_96022
700 _aChowdhury, Shovan
_95419
700 _aJohn Galt Solutions, Inc
_96023
942 _2ddc
_cBK