From a data stream i'm receiving a pair of measurements consisting of a current consumption and a current percentage every second. By accumulating the consumption over time it will represent eventually the maximum capacity when the percentage reaches from 100% to 0%.
I want to predict the maximum capacity in (almost) real time using linear regression with a small sample size window of two percent. However, when i compare the models of these local regressions of every two percent with the model of the whole data regression, i get very different results due to perhaps local fluctuation. (see figure)
Is there a way to bring the local regression models closer to the whole data model? (in a way that i can see the differences due to fluctuation but overall closer predictions to the whole data model)