Mining the stock market (extended abstract): which measure is best?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correlating synchronous and asynchronous data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical dynamics of on-line independent component analysis
The Journal of Machine Learning Research
Fast window correlations over uncooperative time series
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Distributed pattern discovery in multiple streams
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Learning to Trade with Incremental Support Vector Regression Experts
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
An inter-market arbitrage trading system based on extended classifier systems
Expert Systems with Applications: An International Journal
Mining the hedge and arbitrage of the Taiwan foreign exchange market
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
A number of recent emerging applications call for studying data streams, potentially infinite flows of information updated in real-time. When multiple co-evolving data streams are observed, an important task is to determine how these streams depend on each other, accounting for dynamic dependence patterns without imposing any restrictive probabilistic law governing this dependence. In this paper we argue that flexible least squares (FLS), a penalized version of ordinary least squares that accommodates for time-varying regression coefficients, can be deployed successfully in this context. Our motivating application is statistical arbitrage, an investment strategy that exploits patterns detected in financial data streams. We demonstrate that FLS is algebraically equivalent to the well-known Kalman filter equations, and take advantage of this equivalence to gain a better understanding of FLS and suggest a more efficient algorithm. Promising experimental results obtained from a FLS-based algorithmic trading system for the S&P 500 Futures Index are reported.