Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mixtures of ARMA Models for Model-Based Time Series Clustering
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Time series clustering and classification by the autoregressive metric
Computational Statistics & Data Analysis
Clustering heteroskedastic time series by model-based procedures
Computational Statistics & Data Analysis
Time series clustering based on forecast densities
Computational Statistics & Data Analysis
A periodogram-based metric for time series classification
Computational Statistics & Data Analysis
Clustering of time series data-a survey
Pattern Recognition
Simulation and Inference for Stochastic Differential Equations: With R Examples
Simulation and Inference for Stochastic Differential Equations: With R Examples
Stock market co-movement assessment using a three-phase clustering method
Expert Systems with Applications: An International Journal
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A new distance to classify time series is proposed. The underlying generating process is assumed to be a diffusion process solution to stochastic differential equations and observed at discrete times. The mesh of observations is not required to shrink to zero. The new dissimilarity measure is based on the L^1 distance between the Markov operators estimated on two observed paths. Simulation experiments are used to analyze the performance of the proposed distance under several conditions including perturbation and misspecification. As an example, real financial data from NYSE/NASDAQ stocks are analyzed and evidence is provided that the new distance seems capable to catch differences in both the drift and diffusion coefficients better than other commonly used non-parametric distances. Corresponding software is available in the add-on package sde for the R statistical environment.