Time-series forecasting from multiple timeseries

Hello. I am new to machine learning and am unable to find the relevant literature to my problem (probably using wrong keywords). My goal is to predict the most probable label of a series of observations where each observation consists of multiple types and the length of "observation-series" have different lengths.

Currently my dataset looks like the following, Id is the grouping variable of a (consecutive) series of observations, label is the single output that identify.

dataset
Area  MajorAxis  MinorAxis  Label  Id
2391  95.42964935  41.92808151  1  035.mpg
2420  96.32376099  42.31892014  1  035.mpg
3248  129.3288422  56.08081818  1  035.mpg
2435  97.15223694  42.97753525  1  035.mpg
2502  99.36376953  42.99647903  1  035.mpg
2461  97.69940948  43.29359055  1  035.mpg
2366  93.56082153  41.85757446  1  035.mpg
2390  95.42179108  41.92354584  1  035.mpg
2390  95.42179108  41.92354584  1  035.mpg
2734  115.0877533  39.17201614  2  036.mpg
3612  149.0444183  53.27929688  2  036.mpg
3652  149.2009125  52.87094498  2  036.mpg
2719  116.4709473  37.98487854  2  036.mpg
2721  117.0030746  37.89451981  2  036.mpg

Is there any built in method in matlab for dealing with multiple input single output timeseries forcasting of this sort?

Answer 1

[Greg Heath]

The MATLAB timeseries equations are valid for vectors. Therefore, multiple inputs and outputs are easily accommodated.

The only drawback is the equations require all inputs to have the same number of delays. In general, this may be very suboptimal.

Hope this helps.

Thank you for formally accepting my answer

Greg