search

Nixtla / nixtla

TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.
https://docs.nixtla.io
Other
2.31k stars 187 forks source link

Update openapi json file #354

Closed AzulGarza closed 6 months ago

AzulGarza commented 6 months ago

Created by Github action

github-actions[bot] commented 6 months ago
Experiment Results ## Experiment 1: air-passengers ### Description: | variable | experiment | |:--------------|:-------------| | h | 12 | | season_length | 12 | | freq | MS | | level | None | | n_windows | 1 | ### Results: | metric | timegpt-1 | timegpt-1-long-horizon | SeasonalNaive | Naive | |:-----------|------------:|-------------------------:|----------------:|-----------:| | mae | 12.6793 | 11.0623 | 47.8333 | 76 | | mape | 0.027 | 0.0232 | 0.0999 | 0.1425 | | mse | 213.936 | 199.132 | 2571.33 | 10604.2 | | total_time | 5.7725 | 2.4483 | 0.0085 | 0.0052 | ### Plot: ![](https://github.com/Nixtla/nixtla/blob/docs-figs-model-performance//action_files/models_performance/plots/plot_air-passengers_12_12_MS_None_1.png?raw=true) ## Experiment 2: air-passengers ### Description: | variable | experiment | |:--------------|:-------------| | h | 24 | | season_length | 12 | | freq | MS | | level | None | | n_windows | 1 | ### Results: | metric | timegpt-1 | timegpt-1-long-horizon | SeasonalNaive | Naive | |:-----------|------------:|-------------------------:|----------------:|-----------:| | mae | 58.1031 | 58.4587 | 71.25 | 115.25 | | mape | 0.1257 | 0.1267 | 0.1552 | 0.2358 | | mse | 4040.21 | 4110.79 | 5928.17 | 18859.2 | | total_time | 2.1964 | 3.3163 | 0.0054 | 0.0044 | ### Plot: ![](https://github.com/Nixtla/nixtla/blob/docs-figs-model-performance//action_files/models_performance/plots/plot_air-passengers_24_12_MS_None_1.png?raw=true) ## Experiment 3: electricity-multiple-series ### Description: | variable | experiment | |:--------------|:-------------| | h | 24 | | season_length | 24 | | freq | H | | level | None | | n_windows | 1 | ### Results: | metric | timegpt-1 | timegpt-1-long-horizon | SeasonalNaive | Naive | |:-----------|------------:|-------------------------:|----------------:|---------------:| | mae | 178.293 | 196.363 | 269.23 | 1331.02 | | mape | 0.0234 | 0.0234 | 0.0304 | 0.1692 | | mse | 121588 | 123119 | 213677 | 4.68961e+06 | | total_time | 2.1221 | 3.1309 | 0.0074 | 0.0065 | ### Plot: ![](https://github.com/Nixtla/nixtla/blob/docs-figs-model-performance//action_files/models_performance/plots/plot_electricity-multiple-series_24_24_H_None_1.png?raw=true) ## Experiment 4: electricity-multiple-series ### Description: | variable | experiment | |:--------------|:-------------| | h | 168 | | season_length | 24 | | freq | H | | level | None | | n_windows | 1 | ### Results: | metric | timegpt-1 | timegpt-1-long-horizon | SeasonalNaive | Naive | |:-----------|------------:|-------------------------:|----------------:|---------------:| | mae | 465.532 | 353.528 | 398.956 | 1119.26 | | mape | 0.062 | 0.0454 | 0.0512 | 0.1583 | | mse | 835120 | 422332 | 656723 | 3.17316e+06 | | total_time | 2.6091 | 2.882 | 0.0069 | 0.0063 | ### Plot: ![](https://github.com/Nixtla/nixtla/blob/docs-figs-model-performance//action_files/models_performance/plots/plot_electricity-multiple-series_168_24_H_None_1.png?raw=true) ## Experiment 5: electricity-multiple-series ### Description: | variable | experiment | |:--------------|:-------------| | h | 336 | | season_length | 24 | | freq | H | | level | None | | n_windows | 1 | ### Results: | metric | timegpt-1 | timegpt-1-long-horizon | SeasonalNaive | Naive | |:-----------|--------------:|-------------------------:|----------------:|---------------:| | mae | 558.649 | 361.033 | 602.926 | 1340.95 | | mape | 0.0697 | 0.046 | 0.0787 | 0.17 | | mse | 1.22721e+06 | 441118 | 1.61572e+06 | 6.04619e+06 | | total_time | 3.6822 | 2.0621 | 0.0069 | 0.0065 | ### Plot: ![](https://github.com/Nixtla/nixtla/blob/docs-figs-model-performance//action_files/models_performance/plots/plot_electricity-multiple-series_336_24_H_None_1.png?raw=true)
jmoralez commented 6 months ago

@AzulGarza I think we should merge this one since this has the numbers produced by the fixed RNG.