In Phase 2 of the TripDataAnalysis project, building upon the findings and insights gained in Phase 1 (https://github.com/abhiram540/TripDataAnalysis-CityOfLouisville-DocklessVehicles), we will explore advanced time series forecasting methods. The focus of this phase will be to make predictions using FBProphet and Vector Autoregression (VAR) models. FBProphet is a popular library that allows for easy and effective time series forecasting and incorporates many features specifically suited for time series analysis. VAR, on the other hand, is a multivariate time series model that considers the interdependence of multiple variables over time to make predictions. By implementing these two models, we aim to improve the accuracy of our demand predictions and gain a deeper understanding of the relationships between various factors that influence demand in urban neighborhoods.
abhiram540/Time-Series-Analysis-of-Louisville-Dockless-eVehicles-dataset
In Phase 2 of the TripDataAnalysis project, building upon the findings and insights gained in Phase 1 (https://github.com/abhiram540/TripDataAnalysis-CityOfLouisville-DocklessVehicles), we will explore advanced time series forecasting methods. The focus of this phase will be to make predictions using FBProphet and Vector Autoregression (VAR) models
Jupyter Notebook