Time Series Model for Monthly Temperature in England Using Python

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 Temperature in England 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 average temperature in England. The dataset describes a time-series of temperature over 148 years (1723-1970), and there are 2,976 observations. We used the first 80% of the observations for training various models while holding back the remaining observations for validating the final model.

ANALYSIS: The baseline prediction (or persistence) for the dataset resulted in an RMSE of 2.874. After performing a grid search for the most optimal ARIMA parameters, the final ARIMA non-seasonal order was (3, 0, 1) with the seasonal order being (1, 0, 1, 12). Furthermore, the chosen model processed the validation data with an RMSE of 1.331, which was better than the baseline model as expected.

CONCLUSION: For this dataset, ARIMA appeared to be an appropriate modeling technique to use. We should consider using ARIMA for additional modeling and predictions.

Dataset Used: Monthly Temperature in England, January 1923 through December 1970

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.