A New Time Series Mining Approach Applied to Multitemporal Remote Sensing Imagery

Romani, L. A. S.; Ávila, Ana Maria Heuminski de; Chino, Daniel Yoshinobu Takada; Zullo, Jurandir; Chbeir, Richard; Traina, Caetano; Traina, Agma J. M.

doi:10.1109/tgrs.2012.2199501

Cited by 24 publications

(9 citation statements)

References 28 publications

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“…For example, Julea et al (2011) proposed a grouped frequent sequence pattern mining algorithm for agricultural monitoring, which was aimed at extracting an evolution of each grid pixel with time series images [25]; Romani et al (2013) developed a RemoteAgri system to discover the Plateau-Valley-Mountain (P-V-M) association patterns for monitoring sugar cane fields with time series of remote sensing images and found that the P-V-M pattern mainly analyzed the association patterns between two geographical parameters [26]; and Saulquin et al (2014) designed an eventbased mining algorithm for dealing with SST anomalies relative to ENSO events, which considers each one-dimensional time series as a series of significant time-scale events for each grid pixel [12]. Generally, each grid pixel may have several patterns, and each pattern may evolve several geographical parameters; therefore, the complicated association patterns from remote sensing images make it impossible for a user to analyze an entire set and find the most interesting ones [27].…”

Section: Introductionmentioning

confidence: 99%

Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets

Xue

Liu

2017

IJGI

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Abstract:The ENSO (El Niño Southern Oscillation) is the dominant inter-annual climate signal on Earth, and its relationships with marine environments constitute a complex interrelated system. As traditional methods face great challenges in analyzing which, how and where marine parameters change when ENSO events occur, we propose an ENSO-oriented marine spatial association pattern (EOMSAP) mining algorithm for dealing with multiple long-term raster-formatted datasets. EOMSAP consists of four key steps. The first quantifies the abnormal variations of marine parameters into three levels using the mean-standard deviation criteria of time series; the second categorizes La Niña events, neutral conditions, or El Niño events using an ENSO index; then, the EOMSAP designs a linking-pruning-generating recursive loop to generate (m + 1)-candidate association patterns from an m-dimensional one by combining a user-specified support with a conditional support; and the fourth generates strong association patterns according to the userspecified evaluation indicators. To demonstrate the feasibility and efficiency of EOMSAP, we present two case studies with real remote sensing datasets from January 1998 to December 2012: one considers performance analysis relative to the ENSO-Apriori and Apriori methods; and the other identifies marine spatial association patterns within the Pacific Ocean.

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Section: Introductionmentioning

confidence: 99%

Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets

Xue

Liu

2017

IJGI

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“…In principle, those methods are emerged from two different paradigms: classical statistical techniques and computational intelligent techniques. The first mainly includes regression [6][7][8], time series [9][10][11], Kalman filtering [12,13] and semi-parametric [14,15] methods, while the second predominantly goes beyond fuzzy logic [16,17], support vector machine (SVM) [18][19][20], artificial neural networks (ANNs) [21][22][23] and hybrid [24][25][26][27] methods.…”

Section: Introductionmentioning

confidence: 99%

Model‐independent approach for short‐term electric load forecasting with guaranteed error convergence

Shen

Guerrero

et al. 2016

IET Control Theory & Applications

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High precision short-term load forecasting is crucial for enhancing safe and effective operation of power systems. This study presents a new method for short-term load forecasting using the concept of trajectory tracking. Unlike most existing forecasting methods, the proposed one is essentially model independent in that the corresponding forecasting algorithms are derived without the need for the specific load models. Furthermore, based upon Lyapunov stability theory, the prediction error of the proposed method is shown to converge with sufficient accuracy one gives rise to better forecasting performance.Peer ReviewedPostprint (published version

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“…De forma general, las series de tiempo por si solas se reducen a una simple organización temporal de muestras que permiten la descripción de algunos parámetros básicos de un proceso estocástico. No obstante lo anterior, lo que realmente representa un gran aporte en el estudio de este tipo de series son las metodologías y los modelos matemáticos que se han desarrollado en torno a la descripción y predicción de variables aleatorias (Romani et al, 2013), en especial los modelos autorregresivos, de medias móviles y todos los que han derivado de la combinación de estos dos (Min et al, 2004).…”

Section: Introductionunclassified

Desarrollo de un Modelo Predictivo para la Estimación del Comportamiento de Variables en una Infraestructura de Red

2015

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El artículo presenta los resultados encontrados al modelar la variable de red perdida de paquetes (para un flujo de datos de señales de televisión) y rendimiento (para una secuencia de tráfico de video digital) en una topología de red inalámbrica. Esto se hace para estimar su comportamiento futuro haciendo uso de modelos autorregresivos integrados con promedios móviles (ARIMA). Todo esto permite identificar la robustez de este tipo de series de tiempo para ser aplicado en las redes interconectadas con el fin de mejorar su rendimiento. El desarrollo de la propuesta se basó en el uso de la metodología Box-Jenkings. Este tipo de series de tiempo univariables son un buen candidato para generar pronósticos de comportamiento en la redes de telecomunicaciones.

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A New Time Series Mining Approach Applied to Multitemporal Remote Sensing Imagery

Cited by 24 publications

References 28 publications

Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets

Novel Algorithm for Mining ENSO-Oriented Marine Spatial Association Patterns from Raster-Formatted Datasets

Model‐independent approach for short‐term electric load forecasting with guaranteed error convergence

Desarrollo de un Modelo Predictivo para la Estimación del Comportamiento de Variables en una Infraestructura de Red

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