Add Neural Networks functionality #18
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Collaborator
elineii
commented
Oct 18, 2024
•
elineii
commented
- Change pipeline and transformers logic for neural networks compatibility. Introduced new transformers (Missing Values Imputer).
- The logic of estimators has been separated into a model and a validator, both managed by a trainer. There are two types of trainers: ML and DL, which handle the training and prediction cycles.
- Added neural network models: DLinear and PatchTST.
- Added multiple examples: tutorials on strategies, transformers, neural networks. Added a script for benchmarking all available configurations.
- Added logging and asserts on typical issues to enhance usability.
- Added tests for different parts of pipeline.
- Bug fixes.
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Dec 9, 2024
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aayugay99
approved these changes
Jan 21, 2025
…and fix backtest example (Tutorial_1_Quick_start.ipynb)
aayugay99
reviewed
Jan 24, 2025
tsururu/dataset/dataset.py
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| @@ -105,6 +109,10 @@ def __init__( | |||
| print_freq_period_info: bool = True, | |||
| ): | |||
| # Columns typing | |||
| self._auto_type_columns(columns_params, "date", "datetime") | |||
| self._auto_type_columns(columns_params, "id", "categorical") | |||
| self._auto_type_columns(columns_params, "target", "continious") | |||
README.md
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| - _TimeToNumGenerator_ and _DateSeasonsGenerator_: generates seasonal features (e.g., month, quarter, day of the week) from date information. | ||
| - _LabelEncodingTransformer_ and _OneHotEncodingTransformer_: encodes categorical features. | ||
| - _MissingValuesImputer_: handles missing values by imputing them with a chosen strategy. | ||
| - _LagTransformer_: generates for lags. |
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generates lagged features
README.md
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| ## Multi-series prediction strategies: | ||
| - _Local-modelling_: | ||
| - An individual model for each time series. | ||
| - Each time series as independent from others. | ||
| - Each time series is independent from others. |
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Each time series is modeled independently of the others.
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