Time series data captures the evolution of a variable over time, like a movie reel of events. It can be collected regularly (e.g., hourly temperatures) or at any moment (e.g., real-time heart rate). This data has diverse applications, including:
Forecasting future trends: Predict sales, weather, or stock prices based on historical data, identifying long-term patterns, spotting anomalies: Detect unusual events like fraud or equipment failure.
This data-driven approach offers valuable insights for informed decisions across various fields, but challenges like missing data and outliers must be addressed.
Every year, the City of Boston releases its “Employee Earnings Report”, a valuable and extensive collection of payroll data for its workers. This report covers a variety of information, such as the employees’ names, job details, and earnings breakdown. The breakdown of earnings is particularly thorough, including details on base salary, overtime pay, and total compensation for every employee.
This dataset reveals a wealth of valuable insights about the financial landscape of the City’s workforce. By delving into the Yearly Employee Earnings Report, one can uncover a deeper understanding of how wages and compensation are shaped over time. The inclusion of detailed categories such as base salary and overtime further enrich the analysis, allowing for a comprehensive examination of the various factors influencing overall earnings. Notably, the annual nature of the report adds a temporal dimension to the data. Each year’s release serves as a timestamped snapshot of the earnings landscape for City of Boston employees. This temporal structure lays the foundation for time series analysis, providing a framework for examining trends, seasonal patterns, and shifts in compensation trends over the years.
By utilizing time series methods, we have the opportunity to reveal noteworthy trends in base salary, pinpoint peak periods of overtime, and evaluate the overall progression of total compensation. Through the implementation of statistical techniques and modeling, we can make projections on future earning patterns and gain a deeper understanding of the intricate financial landscape within the workforce.