Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | NIPS 2016 Time Series Workshop, 09 December 2016, Barcelona, Spain |
| Editors | Oren Anava, Azadeh Khaleghi, Marco Cuturi, Vitaly Kuznetsov, Alexander Rakhlin |
| Place of Publication | USA |
| Publisher | Proceedings of Machine Learning Research (PMLR) |
| Number of pages | 11 |
| Volume | 55 |
| Publication status | Published - 2016 |
| Event | NIPS Time Series Workshop 2016 - Centre Convencions Internacional Barcelona, Barcelona, Spain Duration: 9 Dec 2016 → 9 Dec 2016 https://sites.google.com/site/nipsts2016/home |
Workshop
| Workshop | NIPS Time Series Workshop 2016 |
|---|---|
| Country | Spain |
| City | Barcelona |
| Period | 9/12/16 → 9/12/16 |
| Internet address |
Cite this
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Sparse and smooth adjustments for coherent forecasts in temporal aggregation of time series. / Ben Taieb, Souhaib.
NIPS 2016 Time Series Workshop, 09 December 2016, Barcelona, Spain. ed. / Oren Anava; Azadeh Khaleghi; Marco Cuturi; Vitaly Kuznetsov; Alexander Rakhlin. Vol. 55 USA : Proceedings of Machine Learning Research (PMLR), 2016.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Sparse and smooth adjustments for coherent forecasts in temporal aggregation of time series
AU - Ben Taieb, Souhaib
PY - 2016
Y1 - 2016
N2 - 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 sparseand 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.
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 sparseand 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.
M3 - Conference Paper
VL - 55
BT - NIPS 2016 Time Series Workshop, 09 December 2016, Barcelona, Spain
A2 - Anava, Oren
A2 - Khaleghi, Azadeh
A2 - Cuturi, Marco
A2 - Kuznetsov, Vitaly
A2 - Rakhlin, Alexander
PB - Proceedings of Machine Learning Research (PMLR)
CY - USA
ER -