# multivariate time series: selecting a predictive model

I have a time series dataset that looks like this

                      x       y     z
t
2017-10-28 00:00:01   0.18    0.01  0.55
2017-10-28 00:00:02   0.20    0.01  0.56
2017-10-28 00:00:03   0.24    0.01  0.57
2017-10-28 00:00:04   0.23    0.02  0.58
2017-10-28 00:00:05   0.26    0.01  0.59
...                   ...     ...   ...
2017-10-28 12:59:08   0.53   -0.03  0.9
2017-10-28 12:59:09   0.56   -0.04  0.89
2017-10-28 01:00:00   0.57   -0.04  ???


give (x) & (y) at time (t) I want a chose a model that will best predict the next value in the sequence (z)

notes:
• the time series is stationary - i have detrended, deaseasonlized, and minmax scaled each feature

• What have you tried? – Demetri Pananos Jul 15 '19 at 2:10
• @DemetriPananos k-nearest neighbor to extrapolate z using n-number of past observations of z. no additional features. got less than satisfactory results – Logarithm Jul 15 '19 at 2:13
• This might be helpful – Demetri Pananos Jul 15 '19 at 2:33
• A VAR (Vector AutoRegression) model could be used. – user2974951 Jul 15 '19 at 6:04