If I have a series of observations of say 100 samples of x and y. Is this enough to predict the 101th y corresponding to a x value?Can I use some part of this data of 100 samples to update some values(Considering that noise exists and some data might be corrupt) ?
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I'm voting to close this question as off-topic because it's not about code. – Jim G. Jul 18 '17 at 00:41
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Stack overflow is directed at coding - so if you have code that you expect to work, and it doesn't, you should post it with your question.
A Kalman filter can help in the problem you describe if you have a model for the dependence of y on x. So, for example, if your model is that: y = a * x + b + Gaussian noise, then the Kalman filter is one way to estimate 'a' and 'b', which then allow you to predict the 101'st y from the 101'st x.
Keith Brodie
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