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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
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Better intro/welcome page for doc #343

Closed marcopeix closed 6 months ago

marcopeix commented 6 months ago

Add pictures of model's architecture Links to various parts of docu

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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 | 23.74 | 23.9665 | 0.0087 | 0.0048 | ### 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 | 26.1054 | 27.135 | 0.006 | 0.005 | ### 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 | 142.394 | 196.363 | 269.23 | 1331.02 | | mape | 0.0203 | 0.0234 | 0.0304 | 0.1692 | | mse | 63464.8 | 123119 | 213677 | 4.68961e+06 | | total_time | 26.8578 | 25.2183 | 0.0079 | 0.0069 | ### 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 | 522.427 | 353.528 | 398.956 | 1119.26 | | mape | 0.069 | 0.0454 | 0.0512 | 0.1583 | | mse | 966295 | 422332 | 656723 | 3.17316e+06 | | total_time | 26.6717 | 27.554 | 0.0075 | 0.0069 | ### 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 | 478.362 | 361.033 | 602.926 | 1340.95 | | mape | 0.0622 | 0.046 | 0.0787 | 0.17 | | mse | 805038 | 441118 | 1.61572e+06 | 6.04619e+06 | | total_time | 26.5582 | 28.5977 | 0.0076 | 0.007 | ### 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)
mergenthaler commented 6 months ago

LGTM! Are you still working on something or why is this a Draft?

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 | 9.1525 | 13.027 | 0.0084 | 0.0044 | ### 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 | 4040.22 | 4110.79 | 5928.17 | 18859.2 | | total_time | 13.1837 | 8.4384 | 0.0054 | 0.0045 | ### 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 | 8.4684 | 9.0477 | 0.0073 | 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 | 835121 | 422332 | 656723 | 3.17316e+06 | | total_time | 16.1628 | 17.2317 | 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 | 18.1958 | 20.9709 | 0.0072 | 0.0064 | ### 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)
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 | 213.935 | 199.132 | 2571.33 | 10604.2 | | total_time | 15.6959 | 21.4544 | 0.0081 | 0.0045 | ### 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 | 4040.22 | 4110.79 | 5928.17 | 18859.2 | | total_time | 23.2845 | 23.2497 | 0.0055 | 0.0047 | ### 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 | 24.526 | 24.0069 | 0.0078 | 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 | 22.6555 | 24.5246 | 0.0075 | 0.0066 | ### 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 | 18.7647 | 22.8742 | 0.0075 | 0.0066 | ### 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)
zhao0415 commented 6 months ago

Is that possible to work with the dataset with missing data for TimeGPT? And how? Thank you so much!