This report analyzes daily bike rental demand in the Washington, DC Capital Bikeshare system using data from 2011–2012. Exploratory analysis identifies seasonal ridership patterns and the relationship between weather conditions and rental activity. Time series forecasting models, including ARIMA and naive benchmarks, are evaluated to produce short-term demand forecasts that can support operational planning, fleet management, and maintenance scheduling.

Forecasts of daily bike rentals using the UCI Bike Sharing dataset (2011-2012). Includes: seasonal and temperature analysis, comparing naive & seasonal baselines with ARIMA and ARIMAX models, cross-validation, and a 60-day forecast. Insights on weekly & seasonal usage, and how to improve forecast accuracy.

bike share case study