Heteroscedastic Sequences: Beyond Gaussianity

Oren Anava, Shie Mannor
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:755-763, 2016.

Abstract

We address the problem of sequential prediction in the heteroscedastic setting, when both the signal and its variance are assumed to depend on explanatory variables. By applying regret minimization techniques, we devise an efficient online learning algorithm for the problem, without assuming that the error terms comply with a specific distribution. We show that our algorithm can be adjusted to provide confidence bounds for its predictions, and provide an application to ARCH models. The theoretic results are corroborated by an empirical study.

Cite this Paper

BibTeX
@InProceedings{pmlr-v48-anava16, title = {Heteroscedastic Sequences: Beyond Gaussianity}, author = {Anava, Oren and Mannor, Shie}, booktitle = {Proceedings of The 33rd International Conference on Machine Learning}, pages = {755--763}, year = {2016}, editor = {Balcan, Maria Florina and Weinberger, Kilian Q.}, volume = {48}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {20--22 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v48/anava16.pdf}, url = {https://proceedings.mlr.press/v48/anava16.html}, abstract = {We address the problem of sequential prediction in the heteroscedastic setting, when both the signal and its variance are assumed to depend on explanatory variables. By applying regret minimization techniques, we devise an efficient online learning algorithm for the problem, without assuming that the error terms comply with a specific distribution. We show that our algorithm can be adjusted to provide confidence bounds for its predictions, and provide an application to ARCH models. The theoretic results are corroborated by an empirical study.} }
Endnote
%0 Conference Paper %T Heteroscedastic Sequences: Beyond Gaussianity %A Oren Anava %A Shie Mannor %B Proceedings of The 33rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2016 %E Maria Florina Balcan %E Kilian Q. Weinberger %F pmlr-v48-anava16 %I PMLR %P 755--763 %U https://proceedings.mlr.press/v48/anava16.html %V 48 %X We address the problem of sequential prediction in the heteroscedastic setting, when both the signal and its variance are assumed to depend on explanatory variables. By applying regret minimization techniques, we devise an efficient online learning algorithm for the problem, without assuming that the error terms comply with a specific distribution. We show that our algorithm can be adjusted to provide confidence bounds for its predictions, and provide an application to ARCH models. The theoretic results are corroborated by an empirical study.
RIS
TY - CPAPER TI - Heteroscedastic Sequences: Beyond Gaussianity AU - Oren Anava AU - Shie Mannor BT - Proceedings of The 33rd International Conference on Machine Learning DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-anava16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 48 SP - 755 EP - 763 L1 - http://proceedings.mlr.press/v48/anava16.pdf UR - https://proceedings.mlr.press/v48/anava16.html AB - We address the problem of sequential prediction in the heteroscedastic setting, when both the signal and its variance are assumed to depend on explanatory variables. By applying regret minimization techniques, we devise an efficient online learning algorithm for the problem, without assuming that the error terms comply with a specific distribution. We show that our algorithm can be adjusted to provide confidence bounds for its predictions, and provide an application to ARCH models. The theoretic results are corroborated by an empirical study. ER -
APA
Anava, O. & Mannor, S.. (2016). Heteroscedastic Sequences: Beyond Gaussianity. Proceedings of The 33rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 48:755-763 Available from https://proceedings.mlr.press/v48/anava16.html.

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