Forecasting

This chapter provides an introduction to time series and foundational algorithms related to and for forecasting. We adopt a pragmatic, first-order approach aimed at capturing the dominant attributes of the time series useful for prediction. Two forecasting methods are developed: Holt-Winters exponen...

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Bibliographic Details
Published inAlgorithms for Data Science pp. 343 - 379
Main Authors Steele, Brian, Chandler, John, Reddy, Swarna
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2016
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Summary:This chapter provides an introduction to time series and foundational algorithms related to and for forecasting. We adopt a pragmatic, first-order approach aimed at capturing the dominant attributes of the time series useful for prediction. Two forecasting methods are developed: Holt-Winters exponential forecasting and linear regression with time-varying coefficients. The first two tutorials, using complaints received by the U.S. Consumer Financial Protection Bureau, instruct the reader on processing data with time attributes and computing autocorrelation coefficients. The following tutorials guide the reader through forecasting using economic and stock price series.
ISBN:3319457950
9783319457956
DOI:10.1007/978-3-319-45797-0_11