Time Series Model for University of Michigan Inflation Expectation Using Python and ARIMA

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

SUMMARY: This project aims to construct a time series prediction model and document the end-to-end steps using a template. The Inflation Expectation dataset from the University of Michigan is a time series situation where we are trying to forecast future outcomes based on past data points.

INTRODUCTION: The problem is forecasting the monthly number of median expected price change next 12 months based on consumers’ surveys. The dataset describes a time-series of percentages over 42 years (1978-2020), and there are 512 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 0.221. After performing a grid search for the most optimal ARIMA parameters, the final ARIMA non-seasonal order was (4, 1, 2). Furthermore, the chosen model processed the validation data with an RMSE of 0.206, which was 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: University of Michigan: Inflation Expectation

Dataset ML Model: Time series forecast with numerical attribute

Dataset Reference: University of Michigan, University of Michigan: Inflation Expectation [MICH], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/MICH, October 24, 2020.

The HTML formatted report can be found here on GitHub.