Meta-learning how to forecast time series
Rob Hyndman () and
George Athanasopoulos ()
No 6/18, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics
A crucial task in time series forecasting is the identification of the most suitable forecasting method. We present a general framework for forecast-model selection using meta-learning. A random forest is used to identify the best forecasting method using only time series features. The framework is evaluated using time series from the M1 and M3 competitions and is shown to yield accurate forecasts comparable to several benchmarks and other commonly used automated approaches of time series forecasting. A key advantage of our proposed framework is that the time-consuming process of building a classifier is handled in advance of the forecasting task at hand.
Keywords: FFORMS (Feature-based FORecast-model Selection); time series features; random forest; algorithm selection problem; classsification. (search for similar items in EconPapers)
JEL-codes: C10 C14 C22 (search for similar items in EconPapers)
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