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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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| Published in: | arXiv.org 2012-11 |
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| creator | Veeramachaneni, Sriharsha |
| description | 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. |
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| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2012-11 |
| issn | 2331-8422 |
| language | eng |
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| source | Publicly Available Content Database |
| subjects | Algorithms Bayesian analysis Conditional probability Machine learning Statistical methods Uncertainty Weather forecasting |
| title | Time-series Scenario Forecasting |
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