# Stepwise-Interpretable-Machine-Learning

**Repository Path**: xlk0101/Stepwise-Interpretable-Machine-Learning

## Basic Information

- **Project Name**: Stepwise-Interpretable-Machine-Learning
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-21
- **Last Updated**: 2021-10-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Stepwise-Interpretable-Machine-Learning

This open-source code for the short-term demand forecasting aims to demonstrate the way of integrating econometric models and deep learning methods, using New York taxi records (yellow taxi and for-hire vehicle (FHV)). 

Published journal paper: Kim, T., Sharda, S., Zhou, X. and Pendyala, R.M., 2020. A stepwise interpretable machine learning framework using linear regression (LR) and long short-term memory (LSTM): City-wide demand-side prediction of yellow taxi and for-hire vehicle (FHV) service. Transportation Research Part C: Emerging Technologies, 120, p.102786. https://doi.org/10.1016/j.trc.2020.102786