Auto_TS
statsforecast
Auto_TS | statsforecast | |
---|---|---|
6 | 58 | |
674 | 3,565 | |
- | 2.7% | |
6.8 | 8.9 | |
1 day ago | 10 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | Apache License 2.0 |
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Auto_TS
statsforecast
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TimeGPT-1
I can't find the TimeGPT-1 model.
LICENSE Apache-2
https://github.com/Nixtla/statsforecast/blob/main/LICENSE
Mentions ARIMA, ETS, CES, and Theta modeling
- Facebook Prophet: library for generating forecasts from any time series data
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Sales forecast for next two years
If you only have historical data: StatsForecast
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Time series and cross validation
I also recommend you check Nixtla's libraries, in particular StatsForecast and HierarchicalForecast. They offer a wide selection of forecasting models, and can work with multiple time series. Given that you're working with many products in a warehouse, I think the hierarchical forecast can be very useful, especially for the short time series (the ones that don't seem to have enough time stamps).
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Demand Planning
If you are mostly worried about time and use python you could try out Nixtla's statsforecast as it is very snappy. https://github.com/Nixtla/statsforecast
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Statistical vs Machine Learning vs Deep Learning Modeling for Time Series Forecasting
I was researching about using deep learning for time series forecasting applications when I came across two experiments by the Nixtla team. They showed that their traditional statistical ensemble (comprised of AutoARIMA, ETS, CES, and DynamicOptimizedTheta) beat a bunch of deep learning models (link) and also the AWS forecast API (link).
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Recommendations for books on working with time series/forecasting problems?
- https://nixtla.github.io/statsforecast/
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XGBoost for time series
Leaving these two repos here for anyone interested in trying decision tree regression or statistical forecasting baselines: - https://nixtla.github.io/mlforecast/ - https://github.com/Nixtla/statsforecast
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[Discussion] Amazon's AutoML vs. open source statistical methods
In this reproducible experiment, we compare Amazon Forecast and StatsForecast a python open-source library for statistical methods.
- Statistical methods outperform Amazon’s ML Forecast
What are some alternatives?
Deep_XF - Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
darts - A python library for user-friendly forecasting and anomaly detection on time series.
Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
mlforecast - Scalable machine 🤖 learning for time series forecasting.
modeltime - Modeltime unlocks time series forecast models and machine learning in one framework
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
ChatLog - ⏳ ChatLog: Recording and Analysing ChatGPT Across Time
nixtla - Python SDK for TimeGPT, a foundational time series model
logbrain - Parsing log files can be a tedious task, especially when dealing with complex log formats. The Log Parser aims to streamline this process by leveraging regular expressions to match and capture relevant fields from log entries. With the extracted data, users can perform further analysis, generate reports, or gain insights from their log files.
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
TimeSynth - A Multipurpose Library for Synthetic Time Series Generation in Python
fable - Tidy time series forecasting