Neural networks generative models for time series

Gatta, Federico; Giampaolo, Fabio; Prezioso, Edoardo; Mei, Gang; Cuomo, Salvatore; Piccialli, Francesco

doi:10.1016/j.jksuci.2022.07.010

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Similar to [28], the authors of [29] showed that a dataset created by a GAN adapted for unevenly sampled time series could achieve similar classification accuracy as the original dataset. In [30], several state-of-the-art neural network-based approaches were explored, leading to the suggestion that performance can be enhanced using a balanced ensemble. Using network traffic data as an example, [31] showed that two publicly available GANs-TimeGAN and DoppleGANger-were able to outperform a probabilistic autoregressive model.…”

Section: Augmentation For Time Series Datamentioning

confidence: 99%

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Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling

Ströbel,

Mau,

Puchta

et al. 2024

MAKE

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This study addresses a significant gap in the field of time series regression modeling by highlighting the central role of data augmentation in improving model accuracy. The primary objective is to present a detailed methodology for systematic sampling of training datasets through data augmentation to improve the accuracy of time series regression models. Therefore, different augmentation techniques are compared to evaluate their impact on model accuracy across different datasets and model architectures. In addition, this research highlights the need for a standardized approach to creating training datasets using multiple augmentation methods. The lack of a clear framework hinders the easy integration of data augmentation into time series regression pipelines. Our systematic methodology promotes model accuracy while providing a robust foundation for practitioners to seamlessly integrate data augmentation into their modeling practices. The effectiveness of our approach is demonstrated using process data from two milling machines. Experiments show that the optimized training dataset improves the generalization ability of machine learning models in 86.67% of the evaluated scenarios. However, the prediction accuracy of models trained on a sufficient dataset remains largely unaffected. Based on these results, sophisticated sampling strategies such as Quadratic Weighting of multiple augmentation approaches may be beneficial.

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Section: Augmentation For Time Series Datamentioning

confidence: 99%

“…This assumption is further emboldened by the results in [8], emphasizing the development of efficient selection and combination strategies for time series augmentation as a future research direction. A deeper analysis of ensemble techniques is further motivated by [30].…”

Section: Summary and Research Deficitmentioning

confidence: 99%

Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling

Ströbel,

Mau,

Puchta

et al. 2024

MAKE

View full text Add to dashboard Cite

show abstract

“…One of the primary reasons for generating synthetic sensor data is the limited availability and diversity of real-world sensor datasets. In many applications, obtaining a sufficiently large and diverse dataset for sensor-based tasks can be challenging, expensive, or simply impractical [9]. Synthetic data generation allows to overcome these limitations by creating a diverse set of sensor measurements that encompass various scenarios, environmental conditions, and sensor modalities.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Sensor Measurement Generation With Noise Learning and Multi-Modal Information

Romanelli,

Martinelli

2023

IEEE Access

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Deep learning has transformed data generation, particularly in creating synthetic sensor data. This capability is invaluable in fields like autonomous driving, robotics, and computer science. To achieve this, we train models using real data, enabling them to replicate sensor data closely. These models introduce variations and noise, enhancing diversity and realism. Prominent techniques, including generative adversarial networks (GANs), variational autoencoders (VAEs), and recurrent neural networks (RNNs), excel in generating synthetic sensor data. Our paper focuses on Autoregressive Convolutional Recurrent Neural Networks (CRNN) for Multivariate Time Series Prediction. We incorporate Denoising Autoencoders (DAE) to mimic real-world noise characteristics. Our model is trained and validated using Ultra Wide Band (UWB) and Ultra High-Frequency Radio Frequency Identification (UHF-RFID) sensor data. It integrates sensor measurements and diverse information sources to produce synthetic data complementing realworld data. While demonstrated with UHF-RFID and UWB sensors, these techniques extend to industrial automation, healthcare and environmental monitoring. While our methodology exhibits broad potential, we present practical demonstrations with UHF-RFID and UWB sensors. Our deep neural network model allows researchers to construct datasets for algorithm validation, eliminating the need for costly and time-consuming data collection.

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Quantifying quality of class-conditional generative models in time series domain

Koochali¹,

Walch²,

Thota³

et al. 2023

Appl Intell

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Despite recent breakthroughs in the domain of implicit generative models, the task of evaluating these models remains a challenging task. With no single metric to assess overall performance, various existing metrics only offer partial information. This issue is further compounded for unintuitive data types such as time series, where manual inspection is infeasible. This deficiency hinders the confident application of modern implicit generative models on time series data. To alleviate this problem, we propose two new metrics, the InceptionTime Score (ITS) and the Fréchet InceptionTime Distance (FITD), to assess the quality of class-conditional generative models on time series data. We conduct extensive experiments on 80 different datasets to study the discriminative capabilities of proposed metrics alongside two existing evaluation metrics: Train on Synthetic Test on Real (TSTR) and Train on Real Test on Synthetic (TRTS). Our evaluations reveal that the proposed assessment evaluation metrics, i.e., ITS and FITD in combination with TSTR, can accurately assess class-conditional generative model performance and detect common issues in implicit generative models. Our findings suggest that the proposed evaluation framework can be a valuable tool for confidently applying modern implicit generative models in time series analysis.

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Neural networks generative models for time series

Cited by 9 publications

References 12 publications

Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling

Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling

Synthetic Sensor Measurement Generation With Noise Learning and Multi-Modal Information

Quantifying quality of class-conditional generative models in time series domain

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