Time Series Forecasting using ML, DL & Stats

Analyzing road traffic patterns to build predictive models for traffic forecasting.

GitHub

Aim

To build a robust forecasting solution that can fairly accurately predict future traffic utilizing Machine Learning (ML), Deep Learning (DL) and Statistical Models.

Metrics to Evaluate Models

  1. Mean Absolute Error (MAE) MAE measures the average magnitude of the errors in a set of predictions, without considering their direction. It’s the average over the test sample of the absolute differences between prediction and actual observation.

  2. Root Mean Squared Error (RMSE) RMSE is the square root of the mean squared error. It provides a measure of how spread out these residuals are, and it is in the same units as the target variable.

  3. Mean Absolute Percentage Error (MAPE) MAPE measures the size of the error in terms of percentage. It is calculated as the average of the absolute percentage errors.

Results

Prophet from Facebook (Meta)

Prophet results

Long Short-Term Memory (LSTM)

LSTM

Read more on GitHub.