Time Series Model for Monthly Car 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 Car Sales dataset is a time series situation where we are trying to forecast future outcomes based on past data points.

Additional Notes: This is a replication, with some small modifications, of Dr. Jason Brownlee’s blog post, How to Grid Search Triple Exponential Smoothing for Time Series Forecasting in Python (https://machinelearningmastery.com/how-to-grid-search-triple-exponential-smoothing-for-time-series-forecasting-in-python/). I plan to leverage Dr. Brownlee’s exponential smoothing or ETS (Error, Trend and Seasonality) tutorial examples and build an ETS-based notebook template for future uses.

INTRODUCTION: The problem is to forecast the monthly number of car sales in Quebec. The dataset described a time-series of car sales over 9 year from 1960 to 1968, and there are 108 observations. We used the first 96 observations for training the model while using the remaining 12 observations for testing the model.

ANALYSIS: The ETS model, which models additive trend with no trend dampening, additive seasonal component, seasonal periods of 12, no BoxCox transform, and bias removed, appeared to have the lowest RMSE at 1523.

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

Dataset Used: Monthly car sales in Quebec, 1960-1968

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.