A data filling methodology for time series based on CNN and (Bi)LSTM neural networks

• URL: http://arxiv.org/abs/2204.09994v1
• Date: Thu, 21 Apr 2022 09:40:30 GMT
• Title: A data filling methodology for time series based on CNN and (Bi)LSTM neural networks
• Authors: Kostas Tzoumpas (1) and Aaron Estrada (1) and Pietro Miraglio (2) and Pietro Zambelli (1) ((1) Eurac Research - Institute for Renewable Energy, Bolzano, Italy (2) Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna, Italy)
• Abstract summary: We develop two Deep Learning models aimed at filling data gaps in time series obtained from monitored apartments in Bolzano, Italy. Our approach manages to capture the fluctuating nature of the data and shows good accuracy in reconstructing the target time series.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the process of collecting data from sensors, several circumstances can affect their continuity and validity, resulting in alterations of the data or loss of information. Although classical methods of statistics, such as interpolation-like techniques, can be used to approximate the missing data in a time series, the recent developments in Deep Learning (DL) have given impetus to innovative and much more accurate forecasting techniques. In the present paper, we develop two DL models aimed at filling data gaps, for the specific case of internal temperature time series obtained from monitored apartments located in Bolzano, Italy. The DL models developed in the present work are based on the combination of Convolutional Neural Networks (CNNs), Long Short-Term Memory Neural Networks (LSTMs), and Bidirectional LSTMs (BiLSTMs). Two key features of our models are the use of both pre- and post-gap data, and the exploitation of a correlated time series (the external temperature) in order to predict the target one (the internal temperature). Our approach manages to capture the fluctuating nature of the data and shows good accuracy in reconstructing the target time series. In addition, our models significantly improve the already good results from another DL architecture that is used as a baseline for the present work.

Related papers

• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• Improving age prediction: Utilizing LSTM-based dynamic forecasting for data augmentation in multivariate time series analysis [16.91773394335563]
  We propose a data augmentation and validation framework that utilizes dynamic forecasting with Long Short-Term Memory (LSTM) networks to enrich datasets. The effectiveness of these augmented datasets was then compared with the original data using various deep learning models designed for chronological age prediction tasks.
  arXiv  Detail & Related papers  (2023-12-11T22:47:26Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting [72.89994745876086]
  This work presents the Online Neuro-Evolution-based Neural Architecture Search (ONE-NAS) algorithm. ONE-NAS is a novel neural architecture search method capable of automatically designing and dynamically training recurrent neural networks (RNNs) for online forecasting tasks. Results demonstrate that ONE-NAS outperforms traditional statistical time series forecasting methods.
  arXiv  Detail & Related papers  (2023-02-20T22:25:47Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection [40.21502451136054]
  This work presents DGHL, a new family of generative models for time series anomaly detection. A top-down Convolution Network maps a novel hierarchical latent space to time series windows, exploiting temporal dynamics to encode information efficiently. Our method outperformed current state-of-the-art models on four popular benchmark datasets.
  arXiv  Detail & Related papers  (2022-02-15T17:19:44Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• CARRNN: A Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data [1.8352113484137622]
  In this paper, a novel deep learning-based model is developed for modeling multiple temporal features in sporadic data. The proposed model, called CARRNN, uses a generalized discrete-time autoregressive model that is trainable end-to-end using neural networks modulated by time lags. It is applied to multivariate time-series regression tasks using data provided for Alzheimer's disease progression modeling and intensive care unit (ICU) mortality rate prediction.
  arXiv  Detail & Related papers  (2021-04-08T12:43:44Z)
• Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML [0.0]
  This paper presents an easy to implement data augmentation method to significantly improve the performance of neural networks. It shows that data augmentation, when paired Automated Machine Learning techniques such as Neural Architecture Search, can help to find the best neural architecture for a given time series.
  arXiv  Detail & Related papers  (2021-03-02T19:20:49Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Progressive Growing of Neural ODEs [7.558546277131641]
  We propose a progressive learning paradigm of NODEs for long-term time series forecasting. Specifically, following the principle of curriculum learning, we gradually increase the complexity of data and network capacity as training progresses. Our experiments with both synthetic data and real traffic data (PeMS Bay Area traffic data) show that our training methodology consistently improves the performance of vanilla NODEs by over 64%.
  arXiv  Detail & Related papers  (2020-03-08T01:15:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.