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1. Recent Advances in Energy Time Series Forecasting.

Author

Martínez-Álvarez, Francisco, Troncoso, Alicia, and Riquelme, José C.

Subjects
*TIME series analysis, *FORECASTING, *PAPER, *ELECTRICAL energy, *SOLAR energy
Abstract

This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI's Energies journal. The special issue took place in 2016 and accepted a total of 21 papers from twelve different countries. Electrical, solar, or wind energy forecasting were the most analyzed topics, introducing brand new methods with very sound results. [ABSTRACT FROM AUTHOR]

Published
2017
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2. Wavelet decomposition and autoregressive model for time series prediction

Author

Ben Mabrouk, A., Ben Abdallah, N., and Dhifaoui, Z.

Subjects
*PAPER, *FIBERS, *WRITING materials & instruments, *ART materials
Abstract

Abstract: In this paper we expose new method to analyse time series. The method is essentially applied for the prediction of time series among approximation and modelisation. It is based on wavelet decomposition combined with autoregressive models. An iterative procedure is applied and the performance of the estimator is measured by standardized error. Finally, a comparison with some existing models and methods has been pointed out to prove the performance of our’s. [Copyright &y& Elsevier]

Published
2008
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3. FIKWaste: A Waste Generation Dataset from Three Restaurant Kitchens in Portugal

Author

Vitor Aguiar, Fábio Vasconcelos, and Lucas Pereira

Subjects
Information Systems and Management, Waste management, business.industry, paper, Big data, waste bin, industrial kitchen, lcsh:Z, lcsh:Bibliography. Library science. Information resources, Computer Science Applications, Waste generation, plastic, dataset, Environmental science, Time series, business, glass, Information Systems, Waste disposal, Volume (compression)
Abstract

In the era of big data and artificial intelligence, public datasets are becoming increasingly important for researchers to build and evaluate their models. This paper presents the FIKWaste dataset, which contains time series data for the volume of waste produced in three restaurant kitchens in Portugal. Organic (undifferentiated) and inorganic (glass, paper, and plastic) waste bins were monitored for a consecutive period of four weeks. In addition to the time series measurements, the FIKWaste dataset contains labels for waste disposal events, i.e., when the waste bins are emptied, and technical and non-technical details of the monitored kitchens.

Published
2021
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