Time-series Scenario Forecasting

Many applications require the ability to judge uncertainty of time-series forecasts. Uncertainty is often specified as point-wise error bars around a mean or median forecast. Due to temporal dependencies, such a method obscures some information. We would ideally have a way to query the posterior pro...

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Bibliographic Details
Published in:arXiv.org 2012-11
Main Author: Veeramachaneni, Sriharsha
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Conditional probability
Machine learning
Statistical methods
Uncertainty
Weather forecasting
Online Access:Get full text
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Summary:Many applications require the ability to judge uncertainty of time-series forecasts. Uncertainty is often specified as point-wise error bars around a mean or median forecast. Due to temporal dependencies, such a method obscures some information. We would ideally have a way to query the posterior probability of the entire time-series given the predictive variables, or at a minimum, be able to draw samples from this distribution. We use a Bayesian dictionary learning algorithm to statistically generate an ensemble of forecasts. We show that the algorithm performs as well as a physics-based ensemble method for temperature forecasts for Houston. We conclude that the method shows promise for scenario forecasting where physics-based methods are absent.
ISSN:2331-8422