meta
hts
| meta | hts | |
|---|---|---|
| 1 | 3 | |
| 74 | 107 | |
| - | - | |
| 7.9 | 0.0 | |
| 17 days ago | over 1 year ago | |
| R | R | |
| GNU General Public License v3.0 only | - |
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meta
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help with meta-analysis in R - pre-post effect sizes and pooled proportion
A good place to start
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).
What are some alternatives?
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dmetar - Official repository for the "dmetar" R package. [Read-Only]
hierarchicalforecast - Probabilistic Hierarchical forecasting 👑 with statistical and econometric methods.