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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!

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Not necessarily. Correlation describes the linear relationship between 2 variables. As such, in the presence of other variables, it is possible for uncorrelated features to have significant explanatory power.

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