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?

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• Do you always have the same number of days, like 3 in your example? And I assume that your training set would have several instances with the same car model right? – Erwan Dec 3 at 1:28
• @Erwan yes always have the same days for all cars , and yes I have several other instances like mode_year, model_type like this . But I'm not sure if my above approach is correct or not ? – angela Dec 3 at 6:26