hts
statsforecast
hts | statsforecast | |
---|---|---|
3 | 58 | |
107 | 3,565 | |
- | 2.7% | |
0.0 | 8.9 | |
over 1 year ago | 7 days ago | |
R | Python | |
- | Apache License 2.0 |
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hts
- Time Series Forecasting Compositional Data - no good package exists?
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[P] Fastest and most accurate version of the Exponential Smoothing (ETS) Algorithm for Python
sadly a lot of statistics research is done with R and is unavailable with Python, hopefully this kind of work will also motivate new libraries for Python. I am particularly interested in hierarchical forecasting. Are there Python alternatives to the hts library?(https://github.com/earowang/hts)
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Can anyone explain me hierarchical time series forecating?
Additionally, you could use one of the more complex methods from the aforementioned hts package. This will allow you to make forecasts on all levels of the hierarchy, and use the bootstrapped errors to make adjustments to all forecasts in the hierarchy using a constrained least-squares approach, in order to make all forecasts sum-consistent (make the aggregates of the forecasts equal the forecasts of the aggregates). This allows you to model cannibalisation effects between different products, for example. However for this to work, you'd need quite good models, as the bootstrapped errors are taken as the 'wiggle room' for the adjustments, which means that if you have a badly fitting model, the adjustments might be quite large and no longer make sense (eg. be negative for a sales forecast).
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?
telegram.bot - Develop a Telegram Bot with R
darts - A python library for user-friendly forecasting and anomaly detection on time series.
rtweet - 🐦 R client for interacting with Twitter's [stream and REST] APIs
mlforecast - Scalable machine 🤖 learning for time series forecasting.
tableone - R package to create "Table 1", description of baseline characteristics with or without propensity score weighting
neuralforecast - Scalable and user friendly neural :brain: forecasting algorithms.
RobinHood - An R interface for the RobinHood.com no commision investing site
nixtla - Python SDK for TimeGPT, a foundational time series model
hierarchicalforecast - Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.
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
meta - Official Git repository of R package meta
fable - Tidy time series forecasting