Meta‐learning how to forecast time series
Features of time series are useful in identifying suitable models for forecasting. We present a general framework, labelled Feature‐based FORecast Model Selection (FFORMS), which selects forecast models based on features calculated from each time series. The FFORMS framework builds a mapping that re...
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| Published in: | Journal of forecasting Vol. 42; no. 6; pp. 1476 - 1501 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Wiley Periodicals Inc
01.09.2023
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| ISSN: | 0277-6693, 1099-131X |
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| Abstract | Features of time series are useful in identifying suitable models for forecasting. We present a general framework, labelled Feature‐based FORecast Model Selection (FFORMS), which selects forecast models based on features calculated from each time series. The FFORMS framework builds a mapping that relates the features of a time series to the “best” forecast model using a classification algorithm such as a random forest. The framework is evaluated using time series from the M‐forecasting competitions and is shown to yield forecasts that are almost as accurate as state‐of‐the‐art methods but are much faster to compute. We use model‐agnostic machine learning interpretability methods to explore the results and to study what types of time series are best suited to each forecasting model. |
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| AbstractList | Features of time series are useful in identifying suitable models for forecasting. We present a general framework, labelled Feature‐based FORecast Model Selection (FFORMS), which selects forecast models based on features calculated from each time series. The FFORMS framework builds a mapping that relates the features of a time series to the “best” forecast model using a classification algorithm such as a random forest. The framework is evaluated using time series from the M‐forecasting competitions and is shown to yield forecasts that are almost as accurate as state‐of‐the‐art methods but are much faster to compute. We use model‐agnostic machine learning interpretability methods to explore the results and to study what types of time series are best suited to each forecasting model. Features of time series are useful in identifying suitable models for forecasting. We present a general framework, labelled Feature‐based FORecast Model Selection (FFORMS), which selects forecast models based on features calculated from each time series. The FFORMS framework builds a mapping that relates the features of a time series to the “best” forecast model using a classification algorithm such as a random forest. The framework is evaluated using time series from the M‐forecasting competitions and is shown to yield forecasts that are almost as accurate as state‐of‐the‐art methods but are much faster to compute. We use model‐agnostic machine learning interpretability methods to explore the results and to study what types of time series are best suited to each forecasting model. |
| Author | Athanasopoulos, George Talagala, Thiyanga S. Hyndman, Rob J. |
| Author_xml | – sequence: 1 givenname: Thiyanga S. surname: Talagala fullname: Talagala, Thiyanga S. email: ttalagala@sjp.ac.lk organization: University of Sri Jayewardenepura – sequence: 2 givenname: Rob J. surname: Hyndman fullname: Hyndman, Rob J. organization: Monash University – sequence: 3 givenname: George surname: Athanasopoulos fullname: Athanasopoulos, George organization: Monash University |
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| Cites_doi | 10.1002/for.3980010202 10.1109/IJCNN.2016.7727376 10.1016/S0169-2070(00)00057-1 10.21105/joss.00359 10.1002/for.3980120104 10.32614/CRAN.package.vip 10.1016/S0169-2070(97)00031-9 10.1016/j.neucom.2008.10.017 10.1016/j.ijforecast.2016.09.004 10.1007/978-0-387-75967-8 10.1002/sam.11461 10.2307/2347679 10.1016/j.ijforecast.2019.04.014 10.1016/S0169-2070(01)00110-8 10.1111/j.1467-9892.1993.tb00139.x 10.1016/j.ijforecast.2019.02.011 10.1109/TKDE.2014.2316504 10.2172/1447470 10.1080/10618600.2014.907095 10.32614/CRAN.package.gratis 10.1287/mnsc.38.10.1394 10.1109/PROC.1982.12435 10.1016/S0065-2458(08)60520-3 10.1016/j.neucom.2009.09.020 10.1007/978-3-540-71918-2 10.1109/ICDMW.2015.104 10.1145/1456650.1456656 10.1016/S0304-4076(00)00030-0 10.1016/j.ijforecast.2021.07.002 10.1214/07-AOAS148 10.1016/j.neucom.2004.03.008 10.1023/A:1010933404324 |
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| SubjectTerms | Agnosticism algorithm selection problem black‐box models Classification Forecasting Machine learning machine learning interpretability Mapping random forest Time Time series visualization |
| Title | Meta‐learning how to forecast time series |
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