facebook/prophet

Use Negative Binomial or Poisson to handle counts data?

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#337 opened on 2017年10月24日

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enhancementhelp wanted

説明

There is a simple regression algorithm for counts data, called Poisson regression. This algorithm assumes that every regressor has a multiplicative effect. It's similar to computing the log of the data, except it works even when the data has zeroes.

It's conceivable that you could replace the Poisson distribution with the more general Negative Binomial distribution. The NB distribution is a generalization of the Poisson that allows the mean to be different from the variance. In contrast, in a Poisson distribution the mean is always the same as the variance.

The main difficulty with just changing the Normal distribution to a Negative Binomial is it's then necessary to add the constraint that $0 < \mu < \sigma^2$.

Does this seem like a good idea?

コントリビューターガイド

技術スタック
python
領域
datamachine learning
Issue 種別
research
難度新しい貢献者にとっての実装難度です。1 は非常に小さな変更、5 は専門的な作業です。
4
推定時間経験ある貢献者が調査、実装、テスト、pull request 準備にかけるおおよその時間範囲です。
half day
活動状況Issue が今どれだけ取り組みやすいかを示します。新しい、活発、古い、ブロック中、またはメンテナー入力待ちなどです。
stale
明確さ期待される変更、受け入れ条件、次の手順がどれだけ明確かを示します。
mostly clear
前提条件
Poisson regressionNegative Binomial distributionPythonbasic statistics
初心者向け度初回貢献者にどれだけ取り組みやすいかを 1-100 で推定したスコアです。
30
調査方針
This issue discusses replacing the Normal distribution with Poisson or Negative Binomial for count data in Prophet. To proceed, first review the current implementation in the Prophet codebase (likely in C++ or Python). Then, explore how to model count data with a Poisson likelihood and consider the Negative Binomial extension with the constraint 0<μ<σ². Look at existing comments for suggestions, particularly the discussion about adding a parameter for dispersion. Finally, benchmark against simple Poisson regression to validate improvement.