Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman

Many applications require forecasts for a hierarchy comprising a set of time series along with aggregates of subsets of these series. Although forecasts can be produced independently for each series in the hierarchy, typically this does not lead to coherent forecasts — the property that forecasts add up appropriately across the hierarchy. State-of-the-art hierarchical forecasting methods usually reconcile these independently generated forecasts to satisfy the aggregation constraints. A fundamental limitation of prior research is that it has looked only at the problem of forecasting the mean of each time series. We consider the situation where probabilistic forecasts are needed for each series in the hierarchy. We define forecast coherency in this setting, and propose an algorithm to compute predictive distributions for each series in the hierarchy. Our algorithm has the advantage of synthesizing information from different levels in the hierarchy through a sparse forecast combination and a probabilistic hierarchical aggregation. We evaluate the accuracy of our forecasting algorithm on both simulated data and large-scale electricity smart meter data. The results show consistent performance gains compared to state-of-the art methods

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  Tag: electricity

14 posts
March 7th, 2017

Coherent Probabilistic Forecasts for Hierarchical Time Series

Souhaib Ben Taieb, James W. Taylor, Rob J. Hyndman Abstract: Many applications require forecasts for a hierarchy comprising a set […]

February 4th, 2016

Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression

Souhaib BenTaieb, Raphael Huser, Rob J. Hyndman and Marc G. Genton IEEE Transactions on Smart Grid (2016), 7(5), 2448-2455. Abstract: […]

January 25th, 2016

Probabilistic Energy Forecasting: Global Energy Forecasting Competition 2014 and Beyond

Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J Hyndman International Journal of Forecasting (2016), 32(3), 896–913. […]

June 23rd, 2015

MEFM: An R package for long-term probabilistic forecasting of electricity demand

International Symposium on Forecasting Riverside, California   I will describe and demonstrate a new open-source R package that implements the […]

June 19th, 2015

Probabilistic forecasting of peak electricity demand

Southern California Edison Rosemead, California   Electricity demand forecasting plays an important role in short-term load allocation and long-term planning […]

June 8th, 2015

STR: A Seasonal-Trend Decomposition Procedure Based on Regression

By Alex Dokumentov and Rob J Hyndman

June 4th, 2015

Probabilistic time series forecasting with boosted additive models: an application to smart meter data

By Souhaib Ben Taieb, Raphael Huser, Rob J Hyndman and Marc G Genton

May 22nd, 2015

Probabilistic forecasting of long-term peak electricity demand

The latest version of my talk on electricity demand forecasting.
Given to the “Monash Energy Materials and Systems Institute”

December 17th, 2014

MEFM package for R

The MEFM package for R includes a set of tools for implementing the Monash Electricity Forecasting Model.

June 17th, 2014

Challenges in forecasting peak electricity demand

A two-part seminar given at the Energy Forum, Valais/Wallis, Switzerland.

May 22nd, 2014

Monash Electricity Forecasting Model

By Rob J Hyndman and Shu Fan

April 1st, 2014

A gradient boosting approach to the Kaggle load forecasting competition

By Souhaib Ben Taieb and Rob J Hyndman

International Journal of Forecasting (2014), 30(2), 382–394.

February 1st, 2012

Short-term load forecasting based on a semi-parametric additive model

Shu Fan and Rob J Hyndman Revised 10 January 2011 IEEE Transactions on Power Systems (2012), 27(1), 134-141. Abstract Short-term […]

October 3rd, 2011

Forecasting electricity demand distributions using a semiparametric additive model

Talk given at University of Melbourne, 11 October 2011. University of Adelaide, 16 March 2012 Monash University, 16 May 2012 […]

June 15th, 2011

Evaluating extreme quantile forecasts

Talk to be given at the International Symposium on Forecasting, Prague, 26–29 June 2011. Slides

March 31st, 2011

The price elasticity of electricity demand in South Australia

Shu Fan and Rob J Hyndman Business and Economic Forecasting Unit, Monash University, Clayton, Victoria 3800, Australia Energy policy (2011), […]

July 21st, 2010

Short-term load forecasting based on a semi-parametric additive model

Shu Fan and Rob J Hyndman 20th Australasian Universities Power Engineering Conference 5-8 December 2010, University of Canterbury, Christchurch, New […]

January 3rd, 2010

Density forecasting for long-term peak electricity demand

Rob J Hyndman and Shu Fan IEEE Transactions on Power Systems, 2010, 25(2), 1142-1153 Abstract: Long-term electricity demand forecasting plays […]

June 23rd, 2009

Extreme forecasting

Keynote address, International Symposium on Forecasting, June 2009. Abstract Extremely bad data, extremely poor methods and extremely difficult problems will […]

June 25th, 2007

Forecasting medium- and long-term peak electricity demand

When: 25 June 2007 Where: International Symposium on Forecasting, New York Abstract: Peak electricity demand forecasting is important in medium […]