Multivariate Time Series Imaging for Short-Term Precipitation Forecasting Using Convolutional Neural Networks

Ojeda, Sun Arthur A.; Solano, Geoffrey A.; Peramo, Elmer C.

doi:10.1109/icaiic48513.2020.9065238

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Finally, for human activity recognition based on raw sensor data, Ceja et al [27] achieved better results using recurrence plots against models that use feature-extraction methods such Deep Belief Network and Multi-layer Perceptron by obtaining a 10-fold cross-validation accuracy of 94.2%. Finally, we extend the methodology of our previous work [28] designed for time series classification applied to the meteorological domain. In this work, our framework for multivariate nonlinear time series forecasting can be used in any domain, as long as the forecasting task can be appropriately represented as described in the succeeding sections.…”

Section: Time Series Imaging and Convolutional Neural Networkmentioning

confidence: 96%

Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting

Ojeda

Peramo²,

Solano

2021

Learning and Analytics in Intelligent Systems

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We present a framework for multivariate nonlinear time series forecasting that utilizes phase space image representations and deep learning. Recurrence plots (RP) are a phase space visualization tool used for the analysis of dynamical systems. This approach takes advantage of recurrence plots that are used as input image representations for a class of deep learning algorithms called convolutional neural networks. We show that by leveraging recurrence plots with optimal embedding parameters, appropriate representations of underlying dynamics are obtained by the proposed autoregressive deep learning model to produce forecasts.

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Section: Time Series Imaging and Convolutional Neural Networkmentioning

confidence: 96%

Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting

Ojeda

Peramo²,

Solano

2021

Learning and Analytics in Intelligent Systems

Self Cite

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“…This maximizes the likelihood of the provided forecasts. Therefore, efforts that might be useful in terms of the stated needs include: the geo-visualization of forecasts based on spatial clustering to reflect the characteristics of adjacent terrains [32][33][34][35][36]; forecast geo-visualization for sparse data [37][38][39][40]; the geo-visualization of the forecasting of criminal activities using ma-chine learning and deep learning techniques [34][35][36][39][40][41][42][43]; event forecasting using classical, improved classical, machine learning, and deep learning techniques for multivariate time series [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60]; and, finally, multivariate time series forecasting with sparse data [61][62][63][64][65].…”

Section: Work Related To the Concept Of Spatiotemporal Predictive Geo...mentioning

confidence: 99%

Spatiotemporal Predictive Geo-Visualization of Criminal Activity for Application to Real-Time Systems for Crime Deterrence, Prevention and Control

Salcedo-Gonzalez

Suarez-Paez

Esteve

et al. 2023

IJGI

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This article presents the development of a geo-visualization tool, which provides police officers or any other type of law enforcement officer with the ability to conduct the spatiotemporal predictive geo-visualization of criminal activities in short and continuous time horizons, according to the real events that are happening: that is, for those geographical areas, time slots, and dates that are of interest to users, with the ability to consider individual events or groups of events. This work used real data collected by the Colombian National Police (PONAL); it constitutes a tool that is especially effective when applied to Real-Time Systems for crime deterrence, prevention, and control. For its creation, the spatial and temporal correlation of the events is carried out and the following deep learning techniques are employed: CNN-1D (Convolutional Neural Network-1D), MLP (multilayer perceptron), LSTM (long short-term memory), and the classical technique of VAR (vector autoregression), due to its appropriate performance in the multi-step and multi-parallel forecasting of multivariate time series with sparse data. This tool was developed with Open-Source Software (OSS) as it is implemented in the Python programming language with the corresponding machine learning libraries. It can be implemented with any geographic information system (GIS) and used in relation to other types of activities, such as natural disasters or terrorist activities.

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Simpler Machine Learning Methods Outperform Deep Learning in Motor Fault Detection

Briza,

Piedad,

Peramo

2024

2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

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Multivariate Time Series Imaging for Short-Term Precipitation Forecasting Using Convolutional Neural Networks

Cited by 3 publications

References 2 publications

Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting

Application of Deep Learning in Recurrence Plots for Multivariate Nonlinear Time Series Forecasting

Spatiotemporal Predictive Geo-Visualization of Criminal Activity for Application to Real-Time Systems for Crime Deterrence, Prevention and Control

Simpler Machine Learning Methods Outperform Deep Learning in Motor Fault Detection

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