There is some motivation in the blog post Fast Python Time-Series Forecasting. All algorithms utilized here can be called the same way using the TimeMachines Python package. However, as indicated in the table, some of these draw an important part of their functionality (if not all) from other packages such as Facebook Prophet, Statsmodels TSA, Flux, PmdArima, Uber Orbit and more. Take relative performance with with a grain of salt, since many packages don't intend completely autonomous use and some are aimed at longer term seasonal forecasts. If you have a suggestion for a package or technique that should be included, please file an issue or, even better, add a skater and make a pull request. There is a guide for contributors and a long list of popular time-series packages.
Some of these methods are used in real-time to provide free prediction to anyone who publishes public data using a community API explained at microprediction.com. See the example crawlers folder for examples of algorithms calling the timemachines package. See the knowledge center or contributor guide for instructions on publishing live data that can influence these ratings.
| Name | Rating | Games | Active | Seconds | Dependencies |
|---|---|---|---|---|---|
| slow_precision_ema_ensemble | 1861.0 | 23049 | yes | 0.3 | timemachines |
| thinking_slow_and_fast | 1813.0 | 29310 | yes | 0.1 | timemachines |
| slow_aggressive_ema_ensemble | 1797.0 | 26092 | yes | 0.3 | timemachines |
| slow_balanced_ema_ensemble | 1792.0 | 29987 | yes | 0.3 | timemachines |
| aggressive_ema_ensemble | 1772.0 | 26879 | yes | 0.3 | timemachines |
| precision_ema_ensemble | 1752.0 | 30644 | yes | 0.2 | timemachines |
| thinking_precision_ensemble | 1733.0 | 1287 | yes | 0.6 | timemachines |
| sluggish_moving_average | 1732.0 | 30718 | yes | 0.0 | timemachines |
| thinking_fast_and_slow | 1730.0 | 24214 | yes | 0.1 | timemachines |
| thinking_slow_and_slow | 1722.0 | 24116 | yes | 0.1 | timemachines |
| quick_precision_ema_ensemble | 1718.0 | 24907 | yes | 0.3 | timemachines |
| slowly_moving_average | 1710.0 | 31684 | yes | 0.0 | timemachines |
| balanced_ema_ensemble | 1706.0 | 27726 | yes | 0.2 | timemachines |
| quick_aggressive_ema_ensemble | 1701.0 | 47934 | yes | 0.3 | timemachines |
| quick_balanced_ema_ensemble | 1698.0 | 23316 | yes | 0.3 | timemachines |
| sk_theta | 1591.0 | 23762 | yes | 0.8 | sktime , timemachines |
| quickly_moving_average | 1553.0 | 29991 | yes | 0.0 | timemachines |
| rvr_slowly_hypocratic | 1505.0 | 11957 | yes | 0.5 | river , timemachines |
| thinking_fast_and_fast | 1487.0 | 23989 | yes | 0.1 | timemachines |
| darts_fft | 1485.0 | 1660 | yes | 0.7 | darts , timemachines |
| rvr_quickly_hypocratic | 1471.0 | 16481 | yes | 0.4 | river , timemachines |
| rapidly_moving_average | 1316.0 | 27881 | yes | 0.0 | timemachines |
| rvr_balanced_ensemble | 1271.0 | 15239 | yes | 0.4 | river , timemachines |
| empirical_last_value | 1075.0 | 18950 | yes | 0.0 | timemachines |
| rvr_p1_d0_q0 | 1067.0 | 17082 | yes | 0.0 | river , timemachines |
| rvr_p5_d0_q0 | 1049.0 | 14434 | yes | 0.1 | river , timemachines |
| rvr_p2_d0_q0 | 1004.0 | 20921 | yes | 0.0 | river , timemachines |
| rvr_p8_d0_q0 | 955.0 | 12534 | yes | 0.1 | river , timemachines |