Sparse and Smooth Adjustments for Coherent Forecasts in Temporal Aggregation of Time Series

Souhaib Ben Taieb
Proceedings of the Time Series Workshop at NIPS 2016, PMLR 55:16-26, 2017.

Abstract

Independent forecasts obtained from different temporal aggregates of a given time series may not be mutually consistent. State-of the art forecasting methods usually apply adjustments on the individual level forecasts to satisfy the aggregation constraints. These adjustments require the estimation of the covariance between the individual forecast errors at all aggregation levels. In order to keep a maximum number of individual forecasts unaffected by estimation errors, we propose a new forecasting algorithm that provides sparse and smooth adjustments while still preserving the aggregation constraints. The algorithm computes the revised forecasts by solving a generalized lasso problem. It is shown that it not only provides accurate forecasts, but also applies a significantly smaller number of adjustments to the base forecasts in a large-scale smart meter dataset.

Cite this Paper

BibTeX
@InProceedings{pmlr-v55-bentaieb16, title = {Sparse and Smooth Adjustments for Coherent Forecasts in Temporal Aggregation of Time Series}, author = {Ben Taieb, Souhaib}, booktitle = {Proceedings of the Time Series Workshop at NIPS 2016}, pages = {16--26}, year = {2017}, editor = {Anava, Oren and Khaleghi, Azadeh and Cuturi, Marco and Kuznetsov, Vitaly and Rakhlin, Alexander}, volume = {55}, series = {Proceedings of Machine Learning Research}, address = {Barcelona, Spain}, month = {09 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v55/bentaieb16.pdf}, url = {https://proceedings.mlr.press/v55/bentaieb16.html}, abstract = {Independent forecasts obtained from different temporal aggregates of a given time series may not be mutually consistent. State-of the art forecasting methods usually apply adjustments on the individual level forecasts to satisfy the aggregation constraints. These adjustments require the estimation of the covariance between the individual forecast errors at all aggregation levels. In order to keep a maximum number of individual forecasts unaffected by estimation errors, we propose a new forecasting algorithm that provides sparse and smooth adjustments while still preserving the aggregation constraints. The algorithm computes the revised forecasts by solving a generalized lasso problem. It is shown that it not only provides accurate forecasts, but also applies a significantly smaller number of adjustments to the base forecasts in a large-scale smart meter dataset.} }
Endnote
%0 Conference Paper %T Sparse and Smooth Adjustments for Coherent Forecasts in Temporal Aggregation of Time Series %A Souhaib Ben Taieb %B Proceedings of the Time Series Workshop at NIPS 2016 %C Proceedings of Machine Learning Research %D 2017 %E Oren Anava %E Azadeh Khaleghi %E Marco Cuturi %E Vitaly Kuznetsov %E Alexander Rakhlin %F pmlr-v55-bentaieb16 %I PMLR %P 16--26 %U https://proceedings.mlr.press/v55/bentaieb16.html %V 55 %X Independent forecasts obtained from different temporal aggregates of a given time series may not be mutually consistent. State-of the art forecasting methods usually apply adjustments on the individual level forecasts to satisfy the aggregation constraints. These adjustments require the estimation of the covariance between the individual forecast errors at all aggregation levels. In order to keep a maximum number of individual forecasts unaffected by estimation errors, we propose a new forecasting algorithm that provides sparse and smooth adjustments while still preserving the aggregation constraints. The algorithm computes the revised forecasts by solving a generalized lasso problem. It is shown that it not only provides accurate forecasts, but also applies a significantly smaller number of adjustments to the base forecasts in a large-scale smart meter dataset.
RIS
TY - CPAPER TI - Sparse and Smooth Adjustments for Coherent Forecasts in Temporal Aggregation of Time Series AU - Souhaib Ben Taieb BT - Proceedings of the Time Series Workshop at NIPS 2016 DA - 2017/02/16 ED - Oren Anava ED - Azadeh Khaleghi ED - Marco Cuturi ED - Vitaly Kuznetsov ED - Alexander Rakhlin ID - pmlr-v55-bentaieb16 PB - PMLR DP - Proceedings of Machine Learning Research VL - 55 SP - 16 EP - 26 L1 - http://proceedings.mlr.press/v55/bentaieb16.pdf UR - https://proceedings.mlr.press/v55/bentaieb16.html AB - Independent forecasts obtained from different temporal aggregates of a given time series may not be mutually consistent. State-of the art forecasting methods usually apply adjustments on the individual level forecasts to satisfy the aggregation constraints. These adjustments require the estimation of the covariance between the individual forecast errors at all aggregation levels. In order to keep a maximum number of individual forecasts unaffected by estimation errors, we propose a new forecasting algorithm that provides sparse and smooth adjustments while still preserving the aggregation constraints. The algorithm computes the revised forecasts by solving a generalized lasso problem. It is shown that it not only provides accurate forecasts, but also applies a significantly smaller number of adjustments to the base forecasts in a large-scale smart meter dataset. ER -
APA
Ben Taieb, S.. (2017). Sparse and Smooth Adjustments for Coherent Forecasts in Temporal Aggregation of Time Series. Proceedings of the Time Series Workshop at NIPS 2016, in Proceedings of Machine Learning Research 55:16-26 Available from https://proceedings.mlr.press/v55/bentaieb16.html.

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