Our business problem is to predict total bicycle demand for the next day using different Day, Weather and past Demand information. While Day information for the next day is known beforehand, Weather information is known only up to the current day. Furthermore, past demand information is available only till the previous day since total demand for current day is not known before day end. Additionally, our business goal revolves around profit maximization where loss due to over or under prediction is asymmetric ($2 for each unit of over-prediction vs. $1 for under-prediction).
We use a combination of ARIMA based time series forecasting, along with five different single model approaches. These methods are Continue reading