Time-Series-Transformer
nixtlats
| Time-Series-Transformer | nixtlats | |
|---|---|---|
| 18 | 2 | |
| 191 | 12 | |
| - | - | |
| 0.0 | 0.0 | |
| over 3 years ago | over 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | GNU General Public License v3.0 or later |
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Time-Series-Transformer
nixtlats
-
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.
What are some alternatives?
tsfresh - Automatic extraction of relevant features from time series:
nixtla - Python SDK for TimeGPT, a foundational time series model
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
pycaret - An open-source, low-code machine learning library in Python
statsforecast - Lightning ⚡️ fast forecasting with statistical and econometric models.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
mlforecast - Scalable machine 🤖 learning for time series forecasting.
stock-prediction-deep-neural-learning - Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting