Univariate Time Series Model for Monthly Rainfall Coppermine 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 Monthly Rainfall Coppermine 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 monthly rainfall. The dataset describes a time series of rainfall (in millimeters) over 44 years (1933-1976), and there are 528 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 12.234. The CNN model processed the same test data with an RMSE of 12.239, which was comparable to the baseline model as expected. In an earlier ARIMA modeling experiment, the best ARIMA model with non-seasonal order of (0, 0, 2) and seasonal order of (1, 0, 1, 12) processed the validation data with an RMSE of 10.73.

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

Dataset Used: Monthly Rainfall Coppermine 1933 through 1976.

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.