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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Published in | Algorithms for Data Science pp. 343 - 379 |
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Main Authors | , , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
2016
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Subjects | |
Online Access | Get full text |
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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. |
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ISBN: | 3319457950 9783319457956 |
DOI: | 10.1007/978-3-319-45797-0_11 |