547 research outputs found
Probabilistic forecast reconciliation with applications to wind power and electric load
New methods are proposed for adjusting probabilistic forecasts to ensure
coherence with the aggregation constraints inherent in temporal hierarchies.
The different approaches nested within this framework include methods that
exploit information at all levels of the hierarchy as well as a novel method
based on cross-validation. The methods are evaluated using real data from two
wind farms in Crete, an application where it is imperative for optimal
decisions related to grid operations and bidding strategies to be based on
coherent probabilistic forecasts of wind power. Empirical evidence is also
presented showing that probabilistic forecast reconciliation improves the
accuracy of both point forecasts and probabilistic forecasts
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures
Hierarchical forecasting problems arise when time series have a natural group
structure, and predictions at multiple levels of aggregation and disaggregation
across the groups are needed. In such problems, it is often desired to satisfy
the aggregation constraints in a given hierarchy, referred to as hierarchical
coherence in the literature. Maintaining hierarchical coherence while producing
accurate forecasts can be a challenging problem, especially in the case of
probabilistic forecasting. We present a novel method capable of accurate and
coherent probabilistic forecasts for hierarchical time series. We call it Deep
Poisson Mixture Network (DPMN). It relies on the combination of neural networks
and a statistical model for the joint distribution of the hierarchical
multivariate time series structure. By construction, the model guarantees
hierarchical coherence and provides simple rules for aggregation and
disaggregation of the predictive distributions. We perform an extensive
empirical evaluation comparing the DPMN to other state-of-the-art methods which
produce hierarchically coherent probabilistic forecasts on multiple public
datasets. Compared to existing coherent probabilistic models, we obtained a
relative improvement in the overall Continuous Ranked Probability Score (CRPS)
of 11.8% on Australian domestic tourism data, and 8.1% on the Favorita grocery
sales dataset.Comment: Probabilistic Hierarchical Forecasting, Neural Networks, Poisson
Mixtures, Preprint submitted to IJ
Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases
Cross-temporal Probabilistic Forecast Reconciliation
Forecast reconciliation is a post-forecasting process that involves
transforming a set of incoherent forecasts into coherent forecasts which
satisfy a given set of linear constraints for a multivariate time series. In
this paper we extend the current state-of-the-art cross-sectional probabilistic
forecast reconciliation approach to encompass a cross-temporal framework, where
temporal constraints are also applied. Our proposed methodology employs both
parametric Gaussian and non-parametric bootstrap approaches to draw samples
from an incoherent cross-temporal distribution. To improve the estimation of
the forecast error covariance matrix, we propose using multi-step residuals,
especially in the time dimension where the usual one-step residuals fail. To
address high-dimensionality issues, we present four alternatives for the
covariance matrix, where we exploit the two-fold nature (cross-sectional and
temporal) of the cross-temporal structure, and introduce the idea of
overlapping residuals. We assess the effectiveness of the proposed
cross-temporal reconciliation approaches through a simulation study that
investigates their theoretical and empirical properties and two empirical
forecasting experiments, using the Australian GDP and the Australian Tourism
Demand datasets. For both applications, the optimal cross-temporal
reconciliation approaches significantly outperform the incoherent base
forecasts in terms of the Continuous Ranked Probability Score and the Energy
Score. Overall, our study expands and unifies the notation for cross-sectional,
temporal and cross-temporal reconciliation, thus extending and deepening the
probabilistic cross-temporal framework. The results highlight the potential of
the proposed cross-temporal forecast reconciliation methods in improving the
accuracy of probabilistic forecasting models
Power Management for Energy Systems
The thesis deals with control methods for flexible and efficient power consumption in commercial refrigeration systems that possess thermal storage capabilities, and for facilitation of more environmental sustainable power production technologies such as wind power. We apply economic model predictive control as the overriding control strategy and present novel studies on suitable modeling and problem formulations for the industrial applications, means to handle uncertainty in the control problems, and dedicated optimization routines to solve the problems involved. Along the way, we present careful numerical simulations with simple case studies as well as validated models in realistic scenarios. The thesis consists of a summary report and a collection of 13 research papers written during the period Marts 2010 to February 2013. Four are published in international peer-reviewed scientific journals and 9 are published at international peer-reviewed scientific conferences
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