Deep Time Series Models for Scarce Data

• URL: http://arxiv.org/abs/2103.09348v1
• Date: Tue, 16 Mar 2021 22:16:54 GMT
• Title: Deep Time Series Models for Scarce Data
• Authors: Qiyao Wang, Ahmed Farahat, Chetan Gupta, Shuai Zheng
• Abstract summary: Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research. Data scarcity is a universal issue that occurs in a vast range of data analytics problems.
• Score: 8.673181404172963
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research. A comprehensive comparison of different time series models, for a considered data analytics task, provides useful guidance on model selection for data analytics practitioners. Data scarcity is a universal issue that occurs in a vast range of data analytics problems, due to the high costs associated with collecting, generating, and labeling data as well as some data quality issues such as missing data. In this paper, we focus on the temporal classification/regression problem that attempts to build a mathematical mapping from multivariate time series inputs to a discrete class label or a real-valued response variable. For this specific problem, we identify two types of scarce data: scarce data with small samples and scarce data with sparsely and irregularly observed time series covariates. Observing that all existing works are incapable of utilizing the sparse time series inputs for proper modeling building, we propose a model called sparse functional multilayer perceptron (SFMLP) for handling the sparsity in the time series covariates. The effectiveness of the proposed SFMLP under each of the two types of data scarcity, in comparison with the conventional deep sequential learning models (e.g., Recurrent Neural Network, and Long Short-Term Memory), is investigated through mathematical arguments and numerical experiments.

Related papers

• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Development of a Neural Network-based Method for Improved Imputation of Missing Values in Time Series Data by Repurposing DataWig [1.8719295298860394]
  Missing values in time series data occur often and present obstacles to successful analysis, thus they need to be filled with alternative values, a process called imputation. Although various approaches have been attempted for robust imputation of time series data, even the most advanced methods still face challenges. I developed tsDataWig (time-series DataWig) by modifying DataWig, a neural network-based method that possesses the capacity to process large datasets. Unlike the original DataWig, tsDataWig can directly handle values of time variables and impute missing values in complex time
  arXiv  Detail & Related papers  (2023-08-18T15:53:40Z)
• DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness [78.98998551326812]
  We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis. We show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
  arXiv  Detail & Related papers  (2022-09-26T21:59:14Z)
• Multi-scale Attention Flow for Probabilistic Time Series Forecasting [68.20798558048678]
  We propose a novel non-autoregressive deep learning model, called Multi-scale Attention Normalizing Flow(MANF) Our model avoids the influence of cumulative error and does not increase the time complexity. Our model achieves state-of-the-art performance on many popular multivariate datasets.
  arXiv  Detail & Related papers  (2022-05-16T07:53:42Z)
• PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series [5.911865723926626]
  Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. We show that PIETS is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.
  arXiv  Detail & Related papers  (2021-09-30T20:01:19Z)
• On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study [65.17429512679695]
  In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Despite ADC's intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models.
  arXiv  Detail & Related papers  (2021-06-02T00:48:33Z)
• Adjusting for Autocorrelated Errors in Neural Networks for Time Series Regression and Forecasting [10.659189276058948]
  We learn the autocorrelation coefficient jointly with the model parameters in order to adjust for autocorrelated errors. For time series regression, large-scale experiments indicate that our method outperforms the Prais-Winsten method. Results across a wide range of real-world datasets show that our method enhances performance in almost all cases.
  arXiv  Detail & Related papers  (2021-01-28T04:25:51Z)
• A Non-linear Function-on-Function Model for Regression with Time Series Data [11.738565299608721]
  We propose a general functional mapping that embraces the function-on-function linear model as a special case. We then propose a non-linear function-on-function model using the fully connected neural network to learn the mapping from data. The effectiveness of the proposed model is demonstrated through the application to two real-world problems.
  arXiv  Detail & Related papers  (2020-11-24T20:51:27Z)
• Time Series Data Imputation: A Survey on Deep Learning Approaches [4.4458738910060775]
  Time series data imputation is a well-studied problem with different categories of methods. Time series methods based on deep learning have made progress with the usage of models like RNN. We will review and discuss their model architectures, their pros and cons as well as their effects to show the development of the time series imputation methods.
  arXiv  Detail & Related papers  (2020-11-23T11:57:27Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• A Multi-Channel Neural Graphical Event Model with Negative Evidence [76.51278722190607]
  Event datasets are sequences of events of various types occurring irregularly over the time-line. We propose a non-parametric deep neural network approach in order to estimate the underlying intensity functions.
  arXiv  Detail & Related papers  (2020-02-21T23:10:50Z)

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.