tsfeatures
TGLSTM
| tsfeatures | TGLSTM | |
|---|---|---|
| 5 | 1 | |
| 323 | 10 | |
| 2.5% | - | |
| 5.0 | 10.0 | |
| 10 days ago | about 4 years ago | |
| Python | Python | |
| Apache License 2.0 | - |
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tsfeatures
- tsfeatures: NEW Data - star count:212.0
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[P] Deep Learning for time series forecasting (neuralforecast, python package)
GluonTS Differences: -GluonTS is written in mxnet, which reduces its adoption. In contrast, NeuralForecast is written in PyTorch. -Including new models in GluonTS tends to be challenging because mxnet 's and the library structure's learning curve are steep. PyTorch-Forecasting Differences: -NeuralForecast hosts some models from our research, including N-HiTS and Transformer-based (Autoformer, Informer, Transformer, etc.) methods specialized in long-horizon forecasting (https://arxiv.org/abs/2201.12886). -And the exogenous variables extension of N-BEATS, the NBEATSx (https://arxiv.org/abs/2104.05522). Extra Features: -NeuralForecast has a wide range of curated datasets used in research to develop and test new models, such as Tourism, M3, M4, M5, EPF, ILI, Traffic, Weather, etc. -NeuralForecast models include reasonable hyperparameter spaces to speed up hyperparameter search, based on our experience. -We include an experiment module that makes it easy to put the entire time series forecasting pipeline into production. -Finally, NeuralForecast is part of a larger ecosystem of time-series analysis and forecasting that includes feature creation (tsfeatures, https://github.com/Nixtla/tsfeatures), machine learning models (mlforecast, https://github.com/Nixtla/mlforecast) and statistical models (statsforecast, https://github.com/Nixtla/statsforecast).
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Automated Time Series Processing and Forecasting
Thanks for your comments.
We agree that in most cases prophet is not a good benchmark; however, we wanted to use it because it is one of the most used libraries in forecasting. For that reason, we also tested the solution against AWS Forecast obtaining better results.
Besides the better performance and scalability, the pipeline we created considering all the stages of time series forecasting: preprocessing (e.g. missing value imputation), creation of static and dynamic features, forecast generation, and finally evaluation using data sets of important competencies. (https://github.com/Nixtla/tsfeatures)
On the deployment side, the entire pipeline can be quickly deployed in the user's cloud using terraform. This allows for less development time. (https://github.com/Nixtla/nixtla)
TGLSTM
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[P] Deep Learning for time series forecasting (neuralforecast, python package)
I'm not current on what the SOTA is, but Time Gated LSTM is one example. Another is Latent ODEs for Irregularly-Sampled Time Series.
What are some alternatives?
darts - A python library for user-friendly forecasting and anomaly detection on time series.
nixtla - Python SDK for TimeGPT, a foundational time series model
mlforecast - Scalable machine 🤖 learning for time series forecasting.
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
tsai - Time series Timeseries Deep Learning Machine Learning Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).