Selection of Time Series Forecasting Models based on Performance Information

Santos, P. V.; Ludermir, Teresa Bernarda; Prudêncio, Ricardo B. C.

doi:10.1109/ichis.2004.86

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Beyond these general-purpose meta-features, many more specific ones were formulated. For streaming data one can use streaming landmarks , for time series data one can compute autocorrelation coefficients or the slope of regression models (Arinze, 1994;dos Santos et al, 2004), and for unsupervised problems one can cluster the data in different ways and extract properties of these clusters . In many applications, domain-specific information can be leveraged as well (Smith-Miles, 2009;Olier et al, 2018).…”

Section: Meta-featuresmentioning

confidence: 99%

“…Meta-models can also generate a ranking of the top-K most promising configurations. One approach is to build a k-nearest neighbor (kNN) meta-model to predict which tasks are similar, and then rank the best configurations on these similar tasks (Brazdil et al, 2003b;dos Santos et al, 2004). This is similar to the work discussed in Section 3.3, but without ties to a follow-up optimization approach.…”

Section: Rankingmentioning

confidence: 99%

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Meta-Learning

Vanschoren¹

2019

The Springer Series on Challenges in Machine Learning

324

166

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Meta-learning, or learning to learn, is the science of systematically observing how different machine learning approaches perform on a wide range of learning tasks, and then learning from this experience, or meta-data, to learn new tasks much faster than otherwise possible. Not only does this dramatically speed up and improve the design of machine learning pipelines or neural architectures, it also allows us to replace hand-engineered algorithms with novel approaches learned in a data-driven way. In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field.

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Section: Meta-featuresmentioning

confidence: 99%

Section: Rankingmentioning

confidence: 99%

Meta-Learning

Vanschoren¹

2019

The Springer Series on Challenges in Machine Learning

324

166

View full text Add to dashboard Cite

show abstract

“…A newer approach that facilitates combinations on a higher level, the meta learning level, is presented in [7] and applied to time series forecasting in [31]. It allows taking relations of individual performances into account by providing a ranking of methods for a particular problem.…”

Section: Experiments Two -Comparing Meta-learning Approachesmentioning

confidence: 99%

“…The ranking is then generated by a variation of the Adjust Ratio of Ratios (ARR), which is applied in a classification context in the original paper [7] and extended by a penalty for time intensity in [31], however, in this experiment, the time dimension was discarded and the SMAPE measure was used instead of classifier success rates to adapt the ranking to regression problems. The pairwise ARR for models m p and m q on series s i is…”

Section: Experiments Two -Comparing Meta-learning Approachesmentioning

confidence: 99%

Meta-learning for time series forecasting and forecast combination

2010

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In research of time series forecasting, a lot of uncertainty is still related to the task of selecting an appropriate forecasting method for a problem. It is not only the individual algorithms that are available in great quantities; combination approaches have been equally popular in the last decades. Alone the question of whether to choose the most promising individual method or a combination is not straightforward to answer. Usually, expert knowledge is needed to make an informed decision, however, in many cases this is not feasible due to lack of resources like time, money and manpower. This work identifies an extensive feature set describing both the time series and the pool of individual forecasting methods. The applicability of different meta-learning approaches are investigated, first to gain knowledge on which model works best in which situation, later to improve forecasting performance. Results show the superiority of a rankingbased combination of methods over simple model selection approaches.

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Arbitrated Ensemble for Solar Radiation Forecasting

Cerqueira

Torgo

Soares

2017

Advances in Computational Intelligence

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Utility companies rely on solar radiation forecasting models to control the supply and demand of energy as well as the operability of the grid. They use these predictive models to schedule power plan operations, negotiate prices in the electricity market and improve the performance of solar technologies in general. This paper proposes a novel method for global horizontal irradiance forecasting. The method is based on an ensemble approach, in which individual competing models are arbitrated by a metalearning layer. The goal of arbitrating individual forecasters is to dynamically combine them according to their aptitude in the input data. We validate our proposed model for solar radiation forecasting using data collected by a real-world provider. The results from empirical experiments show that the proposed method is competitive with other methods, including current state-of-the-art methods used for time series forecasting tasks.

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Selection of Time Series Forecasting Models based on Performance Information

Cited by 8 publications

References 13 publications

Meta-Learning

Meta-Learning

Meta-learning for time series forecasting and forecast combination

Arbitrated Ensemble for Solar Radiation Forecasting

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