Time Series Model for Monthly Sunspot Observation 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 Sunspot Observation 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 number of sunspots that can be observed in a month. The dataset describes a time-series of sunspot counts, and there are 2,820 observations. The source of the dataset is credited to Andrews & Herzberg (1985). 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 prediction (or persistence) for the dataset resulted in an RMSE of 20.09. After performing a grid search for the most optimal ARIMA parameters, the final non-seasonal ARIMA order was (3, 0, 2). Furthermore, the chosen model processed the validation data with an RMSE of 18.32, which was slightly better than the baseline model as expected.

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

Dataset Used: Monthly Sunspot Observation

Dataset ML Model: Time series forecast with numerical attributes

Dataset Reference: https://machinelearningmastery.com/time-series-datasets-for-machine-learning/

The HTML formatted report can be found here on GitHub.