Univariate Time Series Model for Ontario Gasoline Demand Using TensorFlow

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

SUMMARY: The project aims to construct a time series prediction model and document the end-to-end steps using a template. The Ontario Gasoline Demand dataset is a univariate time series situation where we attempt to forecast future outcomes based on past data points.

INTRODUCTION: The problem is to forecast the gasoline demand for Ontario. The dataset describes a time series of gasoline demand (in millions of gallons) between 1960 to 1975, and there are 192 monthly observations. We used the first 80% of the observations for training and testing various models while holding back the remaining observations for validating the final model.

ANALYSIS: The baseline persistence model yielded an RMSE of 15,913. The CNN model processed the same test data with an RMSE of 11,653, which was better than the baseline model as expected. In an earlier ARIMA modeling experiment, the best ARIMA model with non-seasonal order of (2, 1, 0) and seasonal order of (1, 2, 2, 12) processed the validation data with an RMSE of 9,794.

CONCLUSION: For this dataset, the TensorFlow ConvLSTM model achieved an acceptable result, and we should consider using TensorFlow for further modeling.

Dataset Used:  Ontario Gasoline Demand, 1960 through 1975.

Dataset ML Model: Time series forecast with numerical attribute

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.