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POAppropriate ways to smooth a periodic time series?
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copied!<p>I have a periodic time series, of air temperature over several years, and I want to be able to predict future values for it.</p> <p>I've calculated the average over the available years of the value at each hour of the year, which works ok, but it's clearly quite noisy, since I only have 4 years data.</p> <ul> <li>One way forward could be to do gaussian smoothing, but a better option might be to fit a spline to it.</li> <li>I searched and found <code>lm.circular</code>, which looks like a feasible solution candidate.<br> <ul> <li>It doesn't have any way of specifying a formula, so no way of asking it to fit a spline. </li> <li>I tried <code>lm.circular</code> using a 1-order polynomial, but a practical problem arose: it ran out of memory. Note that a standard <code>lm</code> is almost instantaneous, and uses no noticeable memory, on the same data</li> <li>I also tried asking it to fit a Von Mises (type='c-l'), and it asked me for an 'init' parameter, and I couldn't really understand from the description what I was supposed to put into 'init'?</li> </ul></li> <li>Using the normal <code>lm</code> is not really an option, since it gives terrible results at either end of the period.</li> <li>I suppose another possibility is to use ets/HoltWinters, with a timeseries frequency equal to the number of hours in the year?</li> </ul> <p>I'm not quite sure which way is the best way forward, but I suspect that this is a pretty common problem, and there are probably very standard ways of dealing with it?</p>

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