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2021 | OriginalPaper | Chapter

Forecast Aggregation and Error Comparison: An Empirical Study

Authors : German Wehinger, Josh Beal

Published in: Data Science – Analytics and Applications

Publisher: Springer Fachmedien Wiesbaden

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The aim of this paper is to present empirical results associated with forecast performance. It is known that common measures of error fail to be scale invariant, and hence cannot be used to make meaningful error comparisons on forecasts across differing time series. This offers a particular challenge toward forecast improvement when one’s intent is to compare error across different units or granularity. Moreover, although it is prudent to test many forecast methods on a time series, one cannot be sure that a single selected method will not lead to complete forecast failure. We address the aforementioned challenges by analyzing a sizable collection of time series in-house.

Metadata
Title
Forecast Aggregation and Error Comparison: An Empirical Study
Authors
German Wehinger
Josh Beal
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-658-32182-6_5

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