Long-term missing value imputation for time series data using deep neural networks

Park, Jangho; Müller, Juliane; Arora, Bhavna; Faybishenko, Boris; Pastorello, Gilberto; Varadharajan, Charuleka; Sahu, Reetik Kumar; Agarwal, Deborah

doi:10.1007/s00521-022-08165-6

Cited by 10 publications

(5 citation statements)

References 37 publications

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“…• (Park et al, 2023) El problema de valores faltantes en aprendizaje supervisado usando redes neurales.…”

Section: Capítulo IIunclassified

Elementos básicos de Análisis Inteligente de Datos

Jaramillo-Chuqui,

Villarroel-Molina

2023

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Este libro trata sobre conceptos elementales junto con scripts cortos de código basado en R Project para hacer análisis inteligente de datos. La relación entre la teoría y la práctica es fundamental en la comprensión de una disciplina, así la aplicación de procedimientos y funciones específicas en tareas elementales es el propósito de este texto. La idea central del texto tiene origen en la asignatura denominada “Análisis Inteligente de Datos”, una cátedra en la que el profesor aporta con elementos fundamentales basados en conceptos y ejercicios prácticos usando R Project. Hoy en día, la disponibilidad de herramientas para la minería de datos es sin duda muy grande. Usuarios con conocimientos básicos pueden aprovechar de utilitarios intuitivos implementados en poderosos entornos de desarrollo. Nosotros hemos querido dar un enfoque al texto hacia una audiencia con mayor relación a la programación y software. Específicamente que constituya una guía básica para estudiantes que inician en el campo de la Inteligencia de datos.

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“…• (Park et al, 2023) El problema de valores faltantes en aprendizaje supervisado usando redes neurales.…”

Section: Capítulo IIunclassified

Elementos básicos de Análisis Inteligente de Datos

Jaramillo-Chuqui,

Villarroel-Molina

2023

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“…Researchers have proposed methods to predict missing data [7]. Effective missing value prediction methods can make data more complete, which is helpful in subsequent data analysis.…”

Section: Literature Review 21 Research On Missing Value Predictionmentioning

confidence: 99%

“…(6) Save the best odor concentration value; find the coordinates of the individual with the best odor concentration value, and let the other fruit flies fly in that direction. (7) The model begins iterative optimization, repeating Steps 2-6 and assessing whether the odor concentration is better than that of the previous iteration. If it is better, continue to search; otherwise, maintain the position of the previous generation of fruit flies and end the algorithm.…”

Section: Steps and Problems Of Foamentioning

confidence: 99%

Soil Heavy-Metal Pollution Prediction Methods Based on Two Improved Neural Network Models

Wang,

Zhang,

2023

Applied Sciences

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Current soil pollution prediction methods need improvement, especially with regard to accuracy in supplementing missing heavy-metal values in soil, and the accuracy and slow convergence speed of methods for predicting heavy-metal content at unknown points. To reduce costs and improve prediction accuracy, this study used two neural network models (SA-FOA-BP and SE-GCN) to supplement missing heavy-metal values and efficiently predict heavy-metal content in soil. The SA-FOA-BP model combines simulated annealing and fruit fly algorithms to optimize the parameter search method in traditional BP neural networks and improve prediction of missing heavy-metal values in soil. A spatial information fusion graph convolutional network prediction model (SE-GCN) constructs a spatial information encoder that can perceive spatial context information, and embeds it with spatial autocorrelation used for auxiliary learning to predict the heavy-metal content in soil. From the experimental results, the SE-GCN model demonstrates improved performance in terms of evaluation indicators compared with other models. Application analysis of the two improved neural network models was conducted; application scenarios and suitability were analyzed, showing that these models have practical application value for soil pollution prediction.

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“…Antonić and Križan [19] applied ANN for spatiotemporal interpolation of climatic variables and found promising results. Other studies have equally applied ANN and other advanced machine-learning techniques for spatiotemporal interpolations of environmental data leading to satisfactory results [20][21][22][23][24][25][26][27][28][29][30]. Also, most of the existing methods utilize linear interpolation schemes or other linear techniques such as empirical orthogonal function analysis before applying ANN in spatiotemporal predictions of environmental data [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Interpolation of environmental data using deep learning and model inference

Ibebuchi,

Abu

2024

Mach. Learn.: Sci. Technol.

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The temporal resolution of environmental data sets plays a major role in the granularity of the information that can be derived from the data. In most cases, it is required that different data sets have a common temporal resolution to enable their consistent evaluations and applications in making informed decisions. This study leverages deep learning with long short-term memory (LSTM) neural networks and model inference to enhance the temporal resolution of climate datasets, specifically temperature, and precipitation, from daily to sub-daily scales. We trained our model to learn the relationship between daily and sub-daily data, subsequently applying this knowledge to increase the resolution of a separate dataset with a coarser (daily) temporal resolution. Our findings reveal a high degree of accuracy for temperature predictions, evidenced by a correlation of 0.99 and a mean absolute error of 0.21 °C, between the actual and predicted sub-daily values. In contrast, the approach was less effective for precipitation, achieving an explained variance of only 37%, compared to 98% for temperature. Further, besides the sub-daily interpolation of the climate data sets, we adapted our approach to increase the resolution of the Normalized difference vegetation index of Landsat (from 16-day to 5-day interval) using the LSTM model pre-trained from the Sentinel 2 Normalized difference vegetation index - that exists at a relatively higher temporal resolution. The explained variance between the predicted Landsat and Sentinel 1 data is 70% with a mean absolute error of 0.03. These results suggest that our method is particularly suitable for environmental datasets with less pronounced short-term variability, offering a promising tool for improving the resolution and utility of the data.

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Long-term missing value imputation for time series data using deep neural networks

Cited by 10 publications

References 37 publications

Elementos básicos de Análisis Inteligente de Datos

Elementos básicos de Análisis Inteligente de Datos

Soil Heavy-Metal Pollution Prediction Methods Based on Two Improved Neural Network Models

Interpolation of environmental data using deep learning and model inference

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