References
- Arthur, D., and S. Vassilvitskii. 2007. “K-Means++: The Advantages of Careful Seeding.” Paper presented at the 18th Annual ACM SIAM Symposium on Discrete Algorithms, New Orleans, LA, January 7–9.
- Dineen, S. 2013. Probability Theory in Finance: A Mathematical Guide to the Black-Scholes Formula, 2nd ed. United States: American Mathematical Society.
- Golay, X., S. Kollias, G. Stoll, D. Meier, A. Valavanis, and P. Boesiger. 1998. “A New Correlation Based Fuzzy Logic Clustering Algorithm for FMRI.” Magnetic Resonance in Medicine 40:249–260.
- Liao, T.W. 2005. “Clustering of Time Series Data - A Survey.” Pattern Recognition 38:1857–1874.
- MacQueen, J. 1967. “Some Methods for Classification and Analysis of Multivariate Observations.” In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, edited by L. M. Le Cam and J. Neyman, 281–297. Berkeley, CA: University of California Press.
- Maharaj, E.A. 2000. “Clusters of Time Series.” Journal of Classification 17:297–314.
- Ostrovsky, R., Y. Rabani, L.J. Schulman, and C. Swamy. 2012. “The Effectiveness of Lloyd-Type Methods for the k-Means Problem.” Journal of the ACM 59 ( article 28).
- Taylor, S. 2005. Asset Price Dynamics, Volatility, and Prediction. Princeton, NJ: Princeton University Press.
- Tunno, F., C. Gallagher, and R. Lund. 2012. “Arc Length Tests for Equivalent Autocovariances.” Journal of Statistical Computation and Simulation 82:1799–1812.
- Wickramarachchi, T., C. Gallagher, and R. Lund. 2015. “Arc Length Asymptotics for Multivariate Time Series.” Applied Stochastic Models in Business and Industry 31:264–281.