nixtlats
Time-Series-Transformer
| nixtlats | Time-Series-Transformer | |
|---|---|---|
| 2 | 18 | |
| 12 | 191 | |
| - | - | |
| 0.0 | 0.0 | |
| about 2 years ago | over 3 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 or later | MIT License |
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nixtlats
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Automated Time Series Processing and Forecasting
Users can use their own models. Just create a fork of the repo and make the appropriate modifications to include any model the user wants to deploy. On our side, we are working to include Deep Learning models with the nixtlats library (https://github.com/nixtla/nixtlats/) that we also developed.
About benchmarking using statistical models, we highly recommend using statsforecast (https://github.com/Nixtla/statsforecast) that we created. It is designed to be highly efficient in fitting statistical models on millions of time series. More complex models can be built on the results to get a positive Forecast Value Added.
Time-Series-Transformer
What are some alternatives?
nixtla - Python SDK for TimeGPT, a foundational time series model
tsfresh - Automatic extraction of relevant features from time series:
darts - A python library for user-friendly forecasting and anomaly detection on time series.
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
pycaret - An open-source, low-code machine learning library in Python
mlforecast - Scalable machine 🤖 learning for time series forecasting.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
stock-prediction-deep-neural-learning - Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting