ADS-506-Time-Series-Analysis

Forecasting California Unemployment Rates in 2023

This is the final project for the University of San Diego’s ADS 506 Time Series course. The technical workbook can be found at the main GitHub repository.

-- Project Status: [Completed]

Project Introduction and Objective

The preliminary objective of this study is to accurately forecast future unemployment rates in California. From which, the secondary objective was to subjectively deduce the likelihood of a recession occurring in the upcoming year (2023), through the forecasted unemployment rate values. Multiple sources have stated that a recession was likely to occur in 2023. Thus, this study aimed to identify whether unemployment rates, alone, could be indicative of an upcoming recession.

Methods Used

• Exploratory Data Analysis (EDA)
• Pre-Processing
• Series Characterization
• Data Partitioning
• Forecasting Methods

Technologies and Resources

• RStudio - Version 1.4.1717
• R - Version 4.1.1

Project Description

• The California Unemployment Rate dataset is provided by Federal Reserve Economic Data (FRED).
• The following forecasting methods were assessed for optimality in forecasting unemployment rates in California:
  • Naive Forecast
  • Double-Exponential Smoothing
  • Holt-Winters Method
  • ARIMA
  • ARFIMA

Getting Started

1. Clone this repository (For help, refer to this tutorial)
2. Raw data is kept in the GitHub repository and Kaggle.
   • Data
3. Data preprocessing, exploratory data analysis, and forecasting methodology are in the R Markdown File.

Featured R Markdown File

• R Markdown File

Author

• Nicholas Lee