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I am working on a dataset which contains sales of some products. The format is like:
product1_sales | product2_sales | product3_sales | factor1 | factor2
I want to predict future sales, and the only thing I have are the factors for future.
What do you suggest me? Do you think that a linear model is ok?
I also wanted to ask, how can I do this with auto model? I can predict only the data I already know, so have I got to
add some empty queries to my dataset in order to predict them?
Secondly, in another dataset , I have this kind of format:
Speed | Pressure | Temperature
This is about a kind of machines. I also have another dataset which gives alarms if an event happens in a specific timestamp.
So I want to find correlations at first, and then I want to built a model in order to understand every time if something will happen before I have an alarm, in order not to stop the machine. Generally I want my model to recognize the flow of my data and predict what is gonna happen.
To sum up, how can I say to my model for example that if speed is under 10 and pressure over 5 I want you to recognize it as a problem of a kind?
Thanks in advance.