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m4 timeseries benchmark #1277

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m4 timeseries benchmark #1277

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01f7ee3
feat: add new m4 benchmark results
Lopa10ko Mar 22, 2024
80f172f
chore: update FEDOT metrics on m4
Lopa10ko Apr 5, 2024
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51 changes: 51 additions & 0 deletions docs/source/benchmarks/forecasting.rst
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Expand Up @@ -55,3 +55,54 @@ Timeout for Fedot and other frameworks was set by 2 minutes on each series. For

Additionally you can examine papers about Fedot performance on different time series forecasting tasks `[1] <https://link.springer.com/chapter/10.1007/978-3-031-16474-3_45>`__ , `[2] <https://arpgweb.com/journal/7/special_issue/12-2018/5/&page=6>`__, `[3] <https://ieeexplore.ieee.org/document/9870347>`__,
`[4] <https://ieeexplore.ieee.org/document/9870347>`__, `[5] <https://ieeexplore.ieee.org/document/9870347>`__, `[6] <https://www.mdpi.com/2073-4441/13/24/3482/htm>`__, `[7] <https://ieeexplore.ieee.org/abstract/document/9986887>`__.


More M4 benchmarking
~~~~~~~~~~~~~~~~~~~~

This benchmark is based on a unified benchmarking interface provided by the `pytsbe framework <https://github.com/ITMO-NSS-team/pytsbe>`__ (a tool for benchmarking automated time-series forecasting algorithms).
The `pytsbe` tool uses `subsample <https://github.com/ITMO-NSS-team/pytsbe/tree/main/data>`__ from `M4 competition <https://paperswithcode.com/dataset/m4>`__ (sample contains 998 series with daily, weekly, monthly, quarterly, yearly intervals).
The forecasting horizons for each series type are: 6 for yearly series, 8 for quarterly series, 18 for monthly series, 13 for weekly series, and 14 for daily series.
The estimation metric used is Symmetric Mean Absolute Percentage Error (SMAPE).

+-------------+----------+--------+--------+--------+-----------+--------+---------+
| Library | Quantile | Intervals |
+ + +--------+--------+--------+-----------+--------+---------+
| | | Daily | Weekly | Montly | Quarterly | Yearly | Overall |
+=============+==========+========+========+========+===========+========+=========+
| LagLlama | 10 | 1.457 | 3.258 | 5.303 | 5.713 | 11.665 | 2.64 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 50 | 4.513 | 11.167 | 18.534 | 20.027 | 33.141 | 13.036 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 90 | 13.123 | 28.268 | 62.091 | 48.793 | 73.565 | 48.056 |
+-------------+----------+--------+--------+--------+-----------+--------+---------+
| NBEATS | 10 | 0.732 | 1.021 | 1.173 | 1.818 | 3.038 | 1.036 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 50 | 1.948 | 4.384 | 7.628 | 8.193 | 12.648 | 4.643 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 90 | 4.57 | 19.665 | 38.343 | 49.764 | 36.045 | 28.567 |
+-------------+----------+--------+--------+--------+-----------+--------+---------+
| TimeGPT | 10 | 1.687 | 1.272 | 1.134 | 2.459 | 4.179 | 1.536 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 50 | 5.586 | 7.17 | 6.235 | 7.058 | 8.982 | 6.565 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 90 | 15.716 | 23.337 | 35.786 | 28.056 | 32.902 | 26.387 |
+-------------+----------+--------+--------+--------+-----------+--------+---------+
| autogluon | 10 | 0.93 | 0.744 | 1.26 | 2.159 | 2.624 | 1.131 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 50 | 2.37 | 5.96 | 7.402 | 6.168 | 7.598 | 4.704 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 90 | 6.189 | 20.888 | 33.51 | 24.909 | 40.516 | 25.026 |
+-------------+----------+--------+--------+--------+-----------+--------+---------+
| Fedot | 10 | 0.97 | 0.733 | 1.342 | 1.771 | 2.892 | 1.064 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 50 | 2.326 | 4.95 | 7.123 | 6.786 | 8.682 | 4.655 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 90 | 5.398 | 19.131 | 43.519 | 36.36 | 41.147 | 30.29 |
+-------------+----------+--------+--------+--------+-----------+--------+---------+
| repeat_last | 10 | 0.795 | 1.059 | 1.477 | 2.534 | 4.242 | 1.146 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 50 | 2.008 | 5.365 | 7.796 | 7.379 | 9.066 | 5.158 |
+ +----------+--------+--------+--------+-----------+--------+---------+
| | 90 | 4.66 | 22.38 | 37.294 | 27.215 | 33.074 | 25.79 |
+-------------+----------+--------+--------+--------+-----------+--------+---------+
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