Time series forecasting using distribution enhanced linear regression

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7818 LNAI, Issue PART 1, 2013, Pages 484-495

  Ristanoski, Goce ^a,b   Liu, Wei ^b   Bailey, James ^a,b  

^a CSIRO   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Quadratic mean; Samples grouping; Time series prediction

Indexed keywords

FORECASTING TIME SERIES; INTERPRETABILITY; LINEAR MODELING; LINEAR REGRESSION MODELS; PREDICTION ERRORS; QUADRATIC MEAN; TIME SERIES FORECASTING; TIME SERIES PREDICTION;

DATA MINING; FORECASTING; LEARNING ALGORITHMS; TIME SERIES;

LINEAR REGRESSION;

EID: 84893603400     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-37453-1_40     Document Type: Conference Paper

References (21)

1. Tsay, R.S.: Analysis of Financial Time Series. Wiley-Interscience (2005)
2. Hulten, G., Spencer, L., et al.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, pp. 97-106 (2001)
3. Keogh, E., Kasetty, S.: On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration. Data Mining and Knowledge Discovery 7(4), 349-371 (2003)
4. Dong, G., Han, J., et al.: Online mining of changes from data streams: Research problems and preliminary results. In: Proceedings of the 2003 ACM SIGMOD Workshop on Management and Processing of Data Streams (2003)
5. Liu, X., Zhang, R., et al.: Incremental Detection of Distribution Change in Stock Order Streams. In: 26th International Conference on Data Engineering Conference, ICDE (2010)
6. Teo, C.H., Vishwanthan, S.V.N., Smola, A.J., Le, Q.V.: Bundle methods for regularized risk minimization. Journal of Machine Learning Research 11, 311-365 (2010)
7. Liu, W., Chawla, S.: A Quadratic Mean based Supervised Learning Model for Managing Data Skewness. In: Proceedings of the Eleventh SIAM International Conference on Data Mining, pp. 188-198 (2011)
8. Vellaisamy, K., Li, J.: Multidimensional decision support indicator (mDSI) for time series stock trend prediction. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 841-848. Springer, Heidelberg (2007)
9. Cheng, H., Tan, P.-N., Gao, J., Scripps, J.: Multistep-ahead time series prediction. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 765-774. Springer, Heidelberg (2006)
10. Liu, Z., Yu, J.X., Lin, X., Lu, H., Wang, W.: Locating motifs in time-series data. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 343-353. Springer, Heidelberg (2005)
11. Web enabled scientific services and applications, http://www.wessa.net/ stocksdata.wasp
12. Hyndman, R.J.: S&P quarterly index online database, http://robjhyndman.com/tsdldata/data/9-17b.dat
13. Muller, K.-R., Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.: Using Support Vector Machines for Time Series Prediction (2000)
14. Liu, X., Wu, X., Wang, H., Zhang, R., Bailey, J., Kotagiri, R.: Mining distribution change in stock order streams. In: IEEE 26th International Conference on Data Engineering, ICDE (2010)
15. Wilcox, R.R.: Introduction to Robust Estimation and Hypothesis Testing. Elsevier Academic Press, New York (2005)
16. Evgeniou, T., Pontil, M.: Regularized multi-task learning. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 109-117 (2004)
17. Adhikari, R., Agrawal, R.K.: A novel weighted ensemble technique for time series forecasting. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part I. LNCS, vol. 7301, pp. 38-49. Springer, Heidelberg (2012)
18. Khoa, N.L.D., Chawla, S.: Robust outlier detection using commute time and eigenspace embedding. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010, Part II. LNCS, vol. 6119, pp. 422-434. Springer, Heidelberg (2010)
19. Widiputra, H., Pears, R., Kasabov, N.: Multiple time-series prediction through multiple time-series relationships profiling and clustered recurring trends. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 161-172. Springer, Heidelberg (2011)
20. Cheng, H., Tan, P.-N.: Semi-supervised learning with data calibration for long-term time series forecasting. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008)
21. Meesrikamolkul, W., Niennattrakul, V., Ratanamahatana, C.A.: Shape-based clustering for time series data. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012, Part I. LNCS, vol. 7301, pp. 530-541. Springer, Heidelberg (2012)