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

doi:10.1007/978-3-030-29888-3_44

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 proceeding › Conference contribution › peer-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 language	English
Title of host publication	Computer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings
Editors	Mario Vento, Gennaro Percannella
Publisher	Springer Verlag
Pages	542-553
Number of pages	12
ISBN (Print)	9783030298876
DOIs	https://doi.org/10.1007/978-3-030-29888-3_44
State	Published - 2019
Externally published	Yes
Event	18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019 - Salerno, Italy Duration: 3 Sep 2019 → 5 Sep 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11678 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019
Country/Territory	Italy
City	Salerno
Period	3/09/19 → 5/09/19

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-29888-3_44

Cite this

Ramos, M. M. P., Lopez Del Alamo, C. J., & Zapana, R. A. (2019). Forecasting of Meteorological Weather Time Series Through a Feature Vector Based on Correlation. In M. Vento, & G. Percannella (Eds.), Computer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings (pp. 542-553). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11678 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-29888-3_44

Ramos, Mery Milagros Paco ; Lopez Del Alamo, Cristian José ; Zapana, Reynaldo Alfonte. / Forecasting of Meteorological Weather Time Series Through a Feature Vector Based on Correlation. Computer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings. editor / Mario Vento ; Gennaro Percannella. Springer Verlag, 2019. pp. 542-553 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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).",

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Ramos, MMP, Lopez Del Alamo, CJ & Zapana, RA 2019, Forecasting of Meteorological Weather Time Series Through a Feature Vector Based on Correlation. in M Vento & G Percannella (eds), Computer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11678 LNCS, Springer Verlag, pp. 542-553, 18th International Conference on Computer Analysis of Images and Patterns, CAIP 2019, Salerno, Italy, 3/09/19. https://doi.org/10.1007/978-3-030-29888-3_44

Forecasting of Meteorological Weather Time Series Through a Feature Vector Based on Correlation. / Ramos, Mery Milagros Paco; Lopez Del Alamo, Cristian José; Zapana, Reynaldo Alfonte.

Computer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings. ed. / Mario Vento; Gennaro Percannella. Springer Verlag, 2019. p. 542-553 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11678 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Forecasting of Meteorological Weather Time Series Through a Feature Vector Based on Correlation

AU - Ramos, Mery Milagros Paco

AU - Lopez Del Alamo, Cristian José

AU - Zapana, Reynaldo Alfonte

PY - 2019

Y1 - 2019

N2 - 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).

AB - 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).

KW - Correlation

KW - Deep Learning

KW - Feature vector

KW - Forecasting of time series

KW - Non-linear forecast models

KW - Weather forecast

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M3 - Contribución a la conferencia

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SN - 9783030298876

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 542

EP - 553

BT - Computer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings

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A2 - Percannella, Gennaro

PB - Springer Verlag

Y2 - 3 September 2019 through 5 September 2019

ER -

Ramos MMP, Lopez Del Alamo CJ, Zapana RA. Forecasting of Meteorological Weather Time Series Through a Feature Vector Based on Correlation. In Vento M, Percannella G, editors, Computer Analysis of Images and Patterns - 18th International Conference, CAIP 2019, Proceedings. Springer Verlag. 2019. p. 542-553. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-29888-3_44

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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