CitiBike Inventory Prediction¶
A machine learning project for predicting bike station inventory at NYC CitiBike stations using time-series forecasting and Markov chain models.
Contents:
Overview¶
This project implements multiple models to predict how many bikes will be available at each CitiBike station over time:
PersistenceModel: Baseline that assumes inventory stays constant
StationAverageModel: Uses average net flow per station
TemporalFlowModel: Time-conditioned flow predictions
MarkovModel: Markov chain with transition matrices and Monte Carlo simulation
Features¶
Rolling window cross-validation for time-series evaluation
Multiple evaluation metrics (MAE, RMSE, state classification)
Configurable via YAML configuration files
Reproducible random seeds for stochastic models