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Let's say for example that I have a dataset about the cars that a company (e.g. Toyota) produced, over the course of the years 1990 - 2016.

Considering that I have already completed the feature engineering step, is there an algorithm that can output predictions on what attributes future Toyota cars will have?

What I mean is, can we produce output that will have the same features/attributes as the input data-points?

simple example:

For input X0-Xn = [EngineType, #OfWheels, #OfDoors, hasRadio, hasGPS, ProductionYear] --> Produce output Y = [EngineType, #OfWheels, #OfDoors, hasRadio, hasGPS, ProductionYear]

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This can be treated as Multivariate Time Series Forecasting, for this you can look into Vector AutoRegression(VAR). Because there might be some association between attributes which we need to take care of hence we can't treat them as separate time series entities.

If you have found some hierarchical relationship between variable you can also look into hierarchical time series forecasting.

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  • $\begingroup$ Thank you for your answer. Unfortunately I have too little reputation and I cannot upvote. If I don't get any more answers in the next couple of days, I will accept this one. Could you please explain what you mean by "...some association between attributes we need to take care of..."? $\endgroup$ Commented Aug 15, 2020 at 11:15
  • $\begingroup$ What i meant was that these feature can have some association with one another for example engine type can be linked to production year hence we can't treat them as separate entities. $\endgroup$ Commented Aug 15, 2020 at 12:41
  • $\begingroup$ It would be better if you could reduce some features with feature engineering. $\endgroup$ Commented Aug 16, 2020 at 14:25

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