Forecasting of Meteorological Weather Time Series Through a Feature Vector Based on Correlation

Mery Milagros Paco Ramos, Cristian José Lopez Del Alamo, Reynaldo Alfonte Zapana

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Nowadays, the impacts of climate change are harming many countries around the world. For this reason, the scientific community is interested in improving methods to forecast weather events, so it is possible to avoid people from being injured. One important thing in the development of time series forecasting methods is to consider the set of values over time that facilitates the prediction of future value. In this sense, we propose a new feature vector based on the correlation and autocorrelation functions. These measures reflect how the observations of a time series are related to each other. Then, univariate forecasting is performed using Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) deep neural network. Finally, we compared the new model with linear and non-linear models. Reported results exhibit that MLP and LSTM models using the proposed feature vector, they show promising results for univariate forecasting. We tested our method on a real-world dataset from the Fisher weather station (Harvard Forest).

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings
EditorsMario Vento, Gennaro Percannella
PublisherSpringer Verlag
Pages542-553
Number of pages12
ISBN (Print)9783030298876
DOIs
StatePublished - 2019
Externally publishedYes
Event18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 - Salerno, Italy
Duration: 3 Sep 20195 Sep 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11678 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019
Country/TerritoryItaly
CitySalerno
Period3/09/195/09/19

Bibliographical note

Funding Information:
The authors would like to express their sincere gratitude to FONDECYT, which is an initiative of the National Council of Science, Technology and Technological Innovation (CONCYTEC), for promoting and financing collaborative research through the research circle N?148-2015-FONDECYT.

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

  • Correlation
  • Deep Learning
  • Feature vector
  • Forecasting of time series
  • Non-linear forecast models
  • Weather forecast

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