Welcome to the second part of my 3-blog series on creating a robust driver based forecasting engine. The first part gave a brief introduction to time series analysis and gives readers the tools needed to makes sense of time series datasets and cleaning it up (Link here). We will be now looking at the next step in our analysis.

While working with one of the leading data analytics teams in India, I have realized that there are two key elements which lead to actionable insights for our clients: **Feature Engineering **and Feature Selection. Feature engineering refers to the process of creating new variables from existing ones which capture hidden business insights. Feature selection involves making the right choices about which variable to choose for our forecasting models. Both these skills are a combination of art and science which need some practice to perfect.

In this article, we will explore the different types of features which are commonly engineered during forecasting projects and the rationale for using them. We will also look at a comprehensive set of methods that we can use to select the best features and a handy method to combine all the these methods. To dig deeper on feature analysis, one can refer to the book “Feature Engineering and Selection: A Practical Approach for Predictive Models” by Max Kuhn and Kjell Johnson

#feature-selection #time-series-analysis #ai #forecasting #feature-engineering