Time Series Model for California Labor Force Participation 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 California Labor Force Participation 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 labor force participation rate in California. A state’s labor-force participation rate is the number of all employed and unemployed workers divided against the state’s civilian population. The dataset describes a time-series of percentages over 44 years (1976-2019), and there are 528 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 0.238. 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 (2, 0, 2, 12). Furthermore, the chosen model processed the validation data with an RMSE of 0.185, 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: Monthly Labor Force Participation Rate for California

Dataset ML Model: Time series forecast with numerical attributes

Dataset Reference: https://fred.stlouisfed.org/series/LBSNSA06

The HTML formatted report can be found here on GitHub.