Time Series Model for Chicago Fed Hiring Expectations Survey 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 Chicago Fed Hiring Expectations Survey dataset is a time series situation where we attempt to forecast future outcomes based on past data points.

INTRODUCTION: The problem is to forecast the monthly number of business condition surveys conducted by the Chicago Federal Reserve for the area of hiring expectations in the next 12 months. The dataset describes a time-series of survey calculation (between plus 40 and minus 40) for over eight years (2013-2020), and there are 93 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 12.132. After performing a grid search for the most optimal ARIMA parameters, the final ARIMA non-seasonal order was (1, 0, 1). Furthermore, the chosen model processed the validation data with an RMSE of 8.694, 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: Chicago Fed Survey of Business Conditions: Hiring Expectations in the next 12 Months, January 2013 to September 2020

Dataset ML Model: Time series forecast with numerical attribute

Dataset Reference: Federal Reserve Bank of Chicago, Chicago Fed Survey of Business Conditions: Hiring Expectations in the next 12 Months [CFSBCHIRINGEXP], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CFSBCHIRINGEXP, November 4, 2020.

The HTML formatted report can be found here on GitHub.