Wondering how to apply ARIMA and VAR on BikeNYC dataset?

[basharathussain]

When we have following data structure:

BikeNYC dataset is a hdf5 file named NYC14_M16x8_T60_NewEnd.h5, which includes two subsets:

• date: a list of timeslots, which is associated the data.
• data: a 4D tensor of shape (number_of_timeslots, 2, 16, 8), of which data[i] is a 3D tensor of shape (2, 16, 8) at the timeslot date[i], data[i][0] is a 16x8 new-flow matrix and data[i][1] is a 16x8 end-flow matrix.

Wondering how to apply arima and var on BikeNYC dataset when both alrogithms expect 2D data and in here we have more dimensions. How did you apply data conversion in this paper?

[liulingbo918]

The 4D tensors [c, n, h, w] can be reshaped to be 2D data [c, nhw] or [cn, hw]. But the spatial information or temporal information may be loss. The results of ARIMA and VAR are quoted from `Deep spatio-temporal residual networks for citywide crowd flows prediction', and you can refer to this paper.