Temperature Time Series: Pattern Analysis and Forecasting

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Manuel Romano Barbosa (Author) FEUP; António Mendes Lopes (Author) FEUP

Conference proceedings International

Title: Proceedings of 2017 4th Experiment at International Conference: Online Experimentation, exp.at 2017

Pages: 226-231

4th Experiment at International Conference, exp.at 2017

6 June 2017 through 8 June 2017

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-00M-XTM

DOI: 10.1109/expat.2017.7984416

Abstract (EN): This paper uses time-frequency methods and neural networks for the analysis and forecasting of indoor temperature time series. In a first phase, the time series are processed by means of the Fourier transform and the empirical mode decomposition methods to unveil temporal patterns embedded in the data. In a second phase, neural networks are adopted for forecasting future values. The results obtained illustrate the effectiveness of the tools used and motivate further developments based on time-frequency techniques for designing the NN forecasting approach.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6