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In this article we present a method by which we can reduce a time series intoa single point in $\mathbb{R}^{13}$. We have chosen 13 dimensions so as toprevent too many points from being labeled as "noise." When using a Euclidean(or Mahalanobis) metric, a simple clustering algorithm will with near certaintylabel the majority of points as "noise." On pure physical considerations, thisis not possible. Included in our 13 dimensions are four parameters whichdescribe the coefficients of a cubic polynomial attached to a Gaussian pickingup a general trend, four parameters picking up periodicity in a time series,two each for amplitude of a wave and period of a wave, and the final fivereport the "leftover" noise of the detrended and aperiodic time series. Of thefinal five parameters, four are the centralized probabilistic moments, and thefinal for the relative size of the series. The first main contribution of thiswork is to apply a theorem of quantum mechanics about the completeness of thesolutions to the quantum harmonic oscillator on $L^2(\mathbb{R})$ to estimatingtrends in time series. The second main contribution is the method of fittingparameters. After many numerical trials, we realized that methods such aNewton-Rhaphson and Levenberg-Marquardt converge extremely fast if the initialguess is good. Thus we guessed many initial points in our parameter space andcomputed only a few iterations, a technique common in Keogh's work on timeseries clustering. Finally, we have produced a model which gives incrediblyaccurate results quickly. We ackowledge that there are faster methods as wellof more accurate methods, but this work shows that we can still increasecomputation speed with little, if any, cost to accuracy in the sense of dataclustering.

Using Quantum Mechanics to Cluster Time Series