Where’s the Magic? (EMD and SSA in R)

When I first heard of SSA (Singular Spectrum Analysis) and the EMD (Empirical Mode Decomposition) I though surely I’ve found a couple of magical methods for decomposing a time series into component parts (trend, various seasonalities, various cycles, noise). And joy of joys, it turns out that each of these methods is implemented in R packages: Rssa and EMD.

In this posting, I’m going to document some of my explorations of the two methods, to hopefully paint a more realistic picture of what the packages and the methods can actually do. (At least in the hands of a non-expert such as myself.)

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Fun with R and HMM’s

I’m always intrigued by techniques that have cool names: Support Vector Machines, State Space Models, Spectral Clustering, and an old favorite Hidden Markov Models (HMM’s). While going through some of my notes, I stumbled onto a fun experiment with HMM’s where you feed a bunch of English text into a two-state HMM and it will (tend to) discover what letters are vowels.
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Electricity Usage in a High-rise Condo Complex pt 4

This is the fourth article in the series, where the techiness builds to a crescendo. If this is too statistical/programming geeky for you, the next posting will return to a more investigative and analytical flavor. Last time, we looked at a fixed-effects model:

m.fe <- lm (dollars ~ 1 + regime + ratetemp * I(dca - 55))

which looks like a plausible model and whose parameters are all statistically significant. A question that might arise is: why not use a hierarchical (AKA multilevel, mixed-effects) model instead? While we’re at it, why not go full-on Bayesian as well? It just so happens that there is a great new tool called Stan which fits the bill and which also has an rstan package for R.

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