How can we convert time series data to supervised learning problem? I am preparing a data for machine learning model. I want to deal with time series data as normal supervised learning prediction. Let's say I have a data for car speed and I have several cars models such as 

+-----+---------+-------------+
| day |  Model  |   Speed     |
+-----+---------+-------------+
|   1 | Bentley | 20.47 km/h  |
|   2 | Bentley | 32.22 km/h  |
|   3 | Bentley | 23.11 km/h  |
|   1 | BMW     | 37.60 km/h  |
|   2 | BMW     | 27.90 km/h  |
|   3 | BMW     | 40.47 km/h  |


so I want to deal with several model in training so that predict the speed for Bentley and BMW.

I have converted the data for training like this :

+---------+------------+------------+-------------------+
|  Model  |   day_1    |     day_2  |    label == day_3 |
+---------+------------+------------+-------------------+
| Bentley | 20.47 km/h | 32.22 km/h | 23.11 km/h        |
| BMW     | 37.60 km/h | 27.90 km/h | 40.47 km/h        |
+---------+------------+------------+-------------------+


Is it a correct approach?
Since you always have a fixed number of days, I think your approach is good. In order to help the learning algorithm you might consider adding some statistics as features for every instance, for example:


mean of the last N days
difference dayN-day(N-1) (evolution)
...


Of course this can work only if there is actually a dependency between the features and the predicted speed.