Time Series Model for Private Housing Permits for California Using Python and ARIMA

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 Private Housing Permits for California 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 total number of building permits for all structure types for the state of California. The dataset describes a time-series of permits issued over 30 years (1991-2020), and there are 354 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 2153. After performing a grid search for the most optimal ARIMA parameters, the final ARIMA non-seasonal order was (0, 1, 1) with the seasonal order being (1, 0, 2, 12). Furthermore, the chosen model processed the validation data with an RMSE of 1486, which was better than the baseline model as expected.

CONCLUSION: For this dataset, the chosen ARIMA model achieved a satisfactory result, and we should consider using the algorithm for further modeling.

Dataset Used: Monthly New Private Housing Units Authorized by Building Permits for California

Dataset ML Model: Time series forecast with numerical attribute

U.S. Census Bureau, New Private Housing Units Authorized by Building Permits for California [CABPPRIV], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CABPPRIV, August 23, 2020.

The HTML formatted report can be found here on GitHub.