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As summarized in the table of the previous slide, the statistics model is fast to build but not easy to represent complex patterns. Because of that, we consider a regression model as another model such as XGBoost and MLP Regressor. The regression model can fit more complex patterns than the statistics model. To fit the regression model better, we can add external data. For example, the weather significantly impacts on the rental/return patterns of shared bicycles. Thus, we add the weather data to fit the regression model better. Also, current bicycle demands can be affected by the previous status. Therefore, we add the historical data to the model. Therefore, our regression model has several independent variables. Variables related to time are day of the week, hour, holiday. Variables related to weather are temperature, wind direction, wind speed, rain, pressure, humidity. Historical data are the number of rentals and returns in (t-1) step, the number of rentals and returns in (t-2) step. |
IEEE/ICACT20210205 Slide.07
[Big Slide]