Abstract
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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Notes
- 1.
Chapter 12 discusses computational aspects of processing streaming data.
- 2.
We used these data in Sect. 6.5
- 3.
Exercise 9.5 of Chap. 9 asked the reader to program a moving average.
- 4.
This is an example of a time series with time steps of distinctly different lengths.
- 5.
As would be done if the data were arriving in a stream.
- 6.
We’ll do just this in the Chap. 12.
- 7.
The Python function with the same purpose is named type.
- 8.
It’s not necessary to stagger the data because the data set has been constructed from staggered data pairs.
- 9.
This statement is mathematically vague—the influence of recent errors on the determination of \(\widehat{\boldsymbol{\beta }}_{n}^{{\ast}}\) depends on α.
References
A.C. Harvey, Forecasting, Structural Time Series and the Kalman Filter (Cambridge University Press, Cambridge, 1989)
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© 2016 Springer International Publishing Switzerland
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Steele, B., Chandler, J., Reddy, S. (2016). Forecasting. In: Algorithms for Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-45797-0_11
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DOI: https://doi.org/10.1007/978-3-319-45797-0_11
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