Compute time series forecast for number of people entering buildings daily
This experiment train and test a Neural Network Regression model, to predict how many people enter different buildings hourly. The model uses Calt2 dataset from UCI Machine Learning repository. The model assumes weekly seasonality. Train model use the data before latest 7 days, test model use latest 7days data. Using t-Test to determine if two sets of data are significantly different from each other.