I have many time series, with each featuring fifteen data points (x,t). I wish to fit a parameterised model to each data series. At present I'm using particle swarm optimisation (PSO) for this purpose. Within my objective function I quantify fitness using the Bray Curtis distance between a) the data series and b) the prediction corresponding to a candidate solution.(adsbygoogle = window.adsbygoogle || []).push({});

The problem I have is that the PSO fitting does not presently account for uncertainty in either x or t. The variances in x and t are most likely themselves invariant and I'd ideally like to treat each data point as a 2D Gaussian PDF.

My question is this: could Gaussian Processes (GPs) be used within my objective function to _stochastically_ determine the dissimilarity between a 15-point data series and the 15 (or more) point prediction corresponding to a candidate solution?

Thank you in advance for any advice you can offer.

Regards,

Will Furnass

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# Stochastically find dissimilarity between time series data and model prediction

Can you offer guidance or do you also need help?

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