fable
statsforecast
| fable | statsforecast | |
|---|---|---|
| 5 | 58 | |
| 553 | 3,575 | |
| 1.1% | 3.0% | |
| 6.8 | 8.9 | |
| about 2 months ago | 10 days ago | |
| R | Python | |
| GNU General Public License v3.0 only | Apache License 2.0 |
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fable
- Fable: Forecasting Models for Tidy Time Series
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Has anyone ever had luck with the forecasting functionality?
Honestly more doable than you’d think. Download RStudio, copy the code from this page https://fable.tidyverts.org, consult ChatGPT until you’re able to get everything running as expected, sub in your data
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Daily Hospital admits time series forecasting
If you are using R, Rob Hyndman's fable package is the best alternative. If you are looking for a Python library, I recommend StatsForecast.
- Methods for filtering and smoothing time series
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Freedom degrees in ARMA(p,q)
For a throughout discussion of ARIMA models I recommend Hyndman's Forecasting: Principles and Practice book. If you are estimating ARIMA with R check Fable. If you are estimating ARIMA with Python StatsForecast.
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?
modeltime - Modeltime unlocks time series forecast models and machine learning in one framework
darts - A python library for user-friendly forecasting and anomaly detection on time series.
modeltime.ensemble - Time Series Ensemble Forecasting
mlforecast - Scalable machine 🤖 learning for time series forecasting.
timetk - Time series analysis in the `tidyverse`
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
modeltime.gluonts - GluonTS Deep Learning with Modeltime
nixtla - Python SDK for TimeGPT, a foundational time series model
LEMMA-Forecasts - Outputs of the LEMMA model for COVID-19 forecasts
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
flow-forecast - Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
pytorch-forecasting - Time series forecasting with PyTorch