Improve Time Series Forecasting performance with the Facebook Prophet model

Essential guide to time series feature engineering and forecasting

Image by Colin Behrens from Pixabay

Time series forecasting involves model building on historical time-stamped data values and external factors to make scientific predictions that drive future strategic decision-making. Training a robust time-series forecasting model for accurate and reliable predictions is one of the most challenging tasks, given its direct impact on related decisions. The robustness of the time series forecasting model solely depends on the feature engineering and data analysis performed prior to modeling.

In one of the previous articles, I discussed an open-source package tsfresh that can generate hundreds of relevant features for your time series use-case.

Automate Time Series Feature Engineering in a few lines of Python Code

Even after including tsfresh ad external features, sometimes the time-series model does not forecast to match the business expectations. In this article, we will discuss and implement, how to improve the performance of a supervised time-series model using features from the Facebook Prophet model.

Getting Started:

We will use a custom-generated sample time-based dataset with 8 independent features and a continuous dependent feature ‘target’. We will be training a Light-GBM model for different feature engineering strategies:

• Light-GBM with external features
• Light-GBM with external features + lags
• Light-GBM with external features + lags + facebook prophet features

We will be implementing and comparing the performance of each of the above-mentioned feature engineering strategies and come to a conclusion on whether Facebook prophet features are effective in training a robust model.

Data:

The raw time-based data is time-based and has 8 independent features and ‘target’ as dependent features. I have created the hour, day, and…