DeepTimeSeries
Last update Oct.19, 2022
Deep learning library for time series forecasting based on PyTorch. It is under development and the first released version will be announced soon.
Why DeepTimeSeries?
DeepTimeSeries is inspired by libraries such as Darts and
PyTorch Forecasting. So why was DeepTimeSeries developed?
The design philosophy of DeepTimeSeries is as follows:
We present logical guidelines for designing various deep learning models for time series forecasting
Our main target users are intermediate-level users who need to develop deep learning models for time series prediction. We provide solutions to many problems that deep learning modelers face because of the uniqueness of time series data.
We additionally implement a high-level API, which allows comparatively beginners to use models that have already been implemented.
Supported Models
| Model | Target features | Non-target features | Deterministic | Probabilistic |
|---|---|---|---|---|
| MLP | o | o | o | o |
| Dilated CNN | o | o | o | o |
| Vanilla RNN | o | o | o | o |
| LSTM | o | o | o | o |
| GRU | o | o | o | o |
| Transformer | o | o | o | o |
Documentation
1 maintainer
Metadata
1 maintainer
Release files for deep-time-series 0.1.2
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
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| deep_time_series-0.1.2.tar.gz | 11.1 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| deep_time_series-0.1.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 27.7 kB
Release files / deep_time_series-0.1.2.tar.gz
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| Tags | Python 3 |
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