Time Series Model for Monthly Shampoo Sales Using Python and ETS

Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a time series prediction model and document the end-to-end steps using a template. The Monthly Shampoo Sales dataset is a time series situation where we are trying to forecast future outcomes based on past data points.

INTRODUCTION: The problem is to forecast the monthly number of shampoo sales. The dataset described a time-series of monthly shampoo sales for three years, and there are 36 observations. We will use the first 24 observations for training the model while using the remaining 12 observations for testing the model.

ANALYSIS: The ETS model, which models multiplicative trend with no trend dampening, no BoxCox transform, and no bias removal, appeared to have the lowest RMSE at 83.72.

CONCLUSION: For this dataset, the chosen ETS model achieved a satisfactory result and should be considered for further modeling.

Dataset Used: Sales of shampoo over a three-year period

Dataset ML Model: Time series forecast with numerical attributes

Dataset Reference: Rob Hyndman and Yangzhuoran Yang (2018). tsdl: Time Series Data Library. v0.1.0. https://pkg.yangzhuoranyang./tsdl/

The HTML formatted report can be found here on GitHub.