Modern Time Series Forecasting: For Predictive Analytics and Anomaly Detection — NeuralProphet (I)

Chris Kuo/Dr. Dataman
Dataman in AI
Published in
18 min readJan 9, 2024

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Sample eBook chapters (free): https://github.com/dataman-git/modern-time-series/blob/main/20240522beauty_TOC.pdf

eBook on Teachable.com: $22.50
https://drdataman.teachable.com/p/home

The print edition on Amazon.com: $65 https://a.co/d/25FVsMx

Many of you may have heard about the open-source Prophet module for time series forecasting. Prophet sets a paradigm in time series forecasting community. Its interpretability of the forecasts as well as interactive user-interface have been welcomed by many professionals. I covered Prophet In the series “Business Forecasting with Facebook’s Prophet”. Now, with the release of the NeuralProphet module, I am even more excited to write the tutorial series to introduce NeuralProphet to you. If you have never used Prophet before, you can click “Business Forecasting with Facebook’s Prophet”.

You may wonder what’s new in NeuralProphet, or even, what’s new in Prophet. In the tutorial series, I will start with a re-cap of Prophet and introduce NeuralProphet. Then I will add modules in a sequence. Neural Prophet inherits the trend, seasonality, holidays & events of Prophet, and expands to the auto-regressive, lagged regressors, and future regressors…

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