Time Series Model for Measles Cases in New York 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 Measles Cases in New York City 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 number of measles cases in New York City. The dataset describes a time-series of measles cases over 44 years (1928-1972), and there are 534 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 prediction (or persistence) for the dataset resulted in an RMSE of 304. After performing a grid search for the most optimal ARIMA parameters, the final ARIMA non-seasonal order was (2, 0, 2) with the seasonal order being (2, 0, 1, 12). Furthermore, the chosen model processed the validation data with an RMSE of 325, which was no better than the baseline model.

CONCLUSION: For this dataset, the chosen ARIMA model did not achieve a satisfactory result. We should explore different sets of ARIMA parameters and conduct further modeling activities.

Dataset Used: Monthly reported number of cases of measles, New York City, 1928-1972

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.