StreamMARS: A Streaming Multivariate Adaptive Regression Splines Algorithm

Date

2019-12-14

Author

Batmaz, İnci

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Computers and internet have become inevitable parts of our life in the 1990s, and afterwards, bulk of data are started being recorded in digital platforms automatically. To extract meaningful patterns from such data computational methods are developed in data mining and machine learning domains. Multivariate adaptive regression splines (MARS) is one such method successfully applied to off-line static data for prediction. In about last ten years, we face with the big data problem due to the steady increase in the size of the data. Streaming data is a kind of big data collected from sensor networks, production processes, twitter messages etc. Algorithms processing this type of data should consider both memory and time limitations as well as its changing nature with time. We develop a streaming version of a powerful predictive method MARS for estimating model parameters on-line in a temporarily adaptive manner using forgetting factors. Performance of the algorithm developed is tested on simulated data with different dimensions in static, abrupt and smoothly changing environments; as well as on real-life datasets, and also, compared with those of some benchmarking methods such as sliding windows. Results show that StreamMARS is a promising algorithm for predicting streaming big data.

URI

https://hdl.handle.net/11511/72160
http://www.cmstatistics.org/RegistrationsV2/CFE2019/viewSubmission.php?in=361&token=630norqsrrr6190q2775p452ro88938n
http://www.cmstatistics.org/CMStatistics2019/fullprogramme.php
http://www.cfenetwork.org/CFE2019/docs/BoACFECMStatistics2019.pdf?20191121220051

Conference Name

The 13th International Conference on Computational and Financial Econometrics (CFE 2019) (14-16 December 2019)

Collections

Department of Statistics, Conference / Seminar

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Citation Formats

İ. Batmaz, “StreamMARS: A Streaming Multivariate Adaptive Regression Splines Algorithm,” London, UK, 2019, p. 71, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/72160.