Proceedings ArticleDOI
A framework for benchmarking machine learning methods using linear models for univariate time series prediction
Rebecca Salles,Laura Silva de Assis,Gustavo Paiva Guedes,Eduardo Bezerra,Fabio Porto,Eduardo Ogasawara +5 more
- pp 2338-2345
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TLDR
A framework for systematic benchmarking some MLM against well-known Linear Methods (LM), namely Polynomial Regression and models in the ARIMA family, used as BM for univariate time series prediction is implemented within the R-Package named TSPred.Abstract:
Time series prediction has been attracting interest of researchers due to its increasing importance in decision-making activities in many fields of knowledge. The demand for better accuracy in time series prediction furthered the arising of many machine learning time series prediction methods (MLM). Choosing a suitable method for a particular dataset is a challenge and demands established benchmark methods (BM) for performance assessment. Suppose a particular BM is selected, and an experimental comparison is made with a particular MLM. If the latter does not provide better prediction results for the same dataset, this indicates that some improvements are needed for the MLM. Regarding this matter, adopting a well-established, easy to interpret, and tuned BM is desirable. This paper presents a framework for systematic benchmarking some MLM against well-known Linear Methods (LM), namely Polynomial Regression and models in the ARIMA family, used as BM for univariate time series prediction. We implemented such a framework within the R-Package named TSPred. This implementation was evaluated using a wide number of datasets from past prediction competitions. The results show that fittest LM provided by TSPred are adequate BM for univariate time series predictions.read more
Citations
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Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting
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Variable Selection in Time Series Forecasting Using Random Forests
TL;DR: The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables, which could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.
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A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings
TL;DR: In this article, the authors presented a comparative study of different forecasting strategies that can be used to forecast the energy consumption of smart buildings, and showed that strategies based on Machine Learning approaches seem to be more suitable for this task.
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Nonstationary time series transformation methods: An experimental review
TL;DR: This paper provides a review and experimental analysis of methods for transformation of nonstationary time series and a discussion on their advantages and limitations to the problem of time series prediction.
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TSPred: A framework for nonstationary time series prediction
TL;DR: TSPred as discussed by the authors is a framework for non-stationary time series prediction, which is made available as an R-package and provides functions for defining and conducting time-series prediction, including data pre-post processing, decomposition, modeling, prediction, and accuracy assessment.
References
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Rob J. Hyndman,Yeasmin Khandakar +1 more
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