Time Series Model for California Restaurant Employees 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 California Full-Service Restaurant Employees 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 full-service restaurant employees in California. The dataset describes a time-series of people (in thousand) over 30 years (1990-2019), and there are 360 observations. We used the first 80% of the observations for training and testing various models while holding back the last 20% of the observations for validating the final model.

ANALYSIS: The baseline prediction (or persistence) for the dataset resulted in an RMSE of 4.056. After performing a grid search for the most optimal ARIMA parameters, the final ARIMA non-seasonal order was (1, 1, 1) with the seasonal order being (2, 0, 1, 12). Furthermore, the chosen model processed the validation data with an RMSE of 1.740, 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 or actual forecasting.

Dataset Used: FRED: All Employees: Full-Service Restaurants in California (SMU06000007072251101)

Dataset ML Model: Time series forecast with numerical attributes

Dataset Reference: https://fred.stlouisfed.org/series/SMU06000007072251101

The HTML formatted report can be found here on GitHub.