This is a technical/conceptual question. I am not sure if this is the right place to ask. If not, please let me know, I will change it.
Question: I have some time series data with 12 room temperatures of a house. All are real number float32 values, ranging -5degC to 30degC.
I was thinking to create a prediction model for one of the room temperatures by using the other room temperatures as input. Now, when I calculate the correlation of the input features with desired output room temperature, using the Pearson method, they are usually very high. There are still 2-3 rooms, that are not very correlated.
Now, my understanding says, that if I use the features as input to the model whose correlation values are high, I will have better accuracy on a model, compared to the low correlated features.
Is it right? I am doing a prediction model to forecast future temperatures.
Let me know your thoughts!