A Dilated Convolutional Based Model for Time Series Forecasting

Mishra, Kakuli; Basu, Srinka; Maulik, Ujjwal

doi:10.1007/s42979-021-00464-4

Cited by 2 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach originally employed a non-linear activation function for time series prediction. Subsequently, the method has been adapted and utilized in various domains, such as financial forecasting in Li et al (2021) and time series forecasting in smart grid applications in Mishra et al (2021). The problem of adaptive filter length is resolved by allowing a substantial increase in the dilated CNN receptive field while keeping the number of optimization parameters very small.…”

Section: Dilated Cnnmentioning

confidence: 99%

Vibration Signal Decomposition using Dilated CNN

Gildish,

Grebshtein,

Aperstein

et al. 2023

PHM_CONF

View full text Add to dashboard Cite

Vibration sensors have gained increasing popularity as valuable tools for Prognostics and Health Management (PHM) applications, enabling early detection of mechanical failures in industrial machines. Vibration signals comprise two main sources of information: periodic vibrations from components, phase-locked to the rotating speed (e.g., gears), and non-deterministic broadband vibrations associated with bearings, structure, and background noise. In PHM applications, it is important to decompose vibrations into these two sources to optimize the use of different diagnostic methods for each signal component. In practice, the decomposition should be cost-effective by working without supplementary information about system operating conditions and kinematics. Existing methods of vibration source separation commonly rely on an auto-regression (AR) model of vibrations and employ adaptive filtering techniques to estimate its parameters. However, these methods suffer from degraded accuracy in complex geared vibrations containing numerous periodic components and requiring large filter length to promise high frequency resolution in component separation. To address these challenges, we propose a new method that utilizes dilated Convolutional Neural Networks (CNNs) instead of adaptive filtering to improve the accuracy of decomposing complex vibration signals, all without the need for any supplementary information. To evaluate the performance of the new method, we conducted experiments using both simulated signals and real-world vibrations. The simulation results demonstrate improved accuracy in signal decomposition when our method is used instead of adaptive filtering. Additionally, the new method applied to real vibrations, showcases significant enhancement in bearing failure detection through accurate isolation of bearing-related vibrations. This study reveals the potential of our new method in various PHM applications requiring highly accurate diagnostics and prognostics in complex geared vibrations, particularly when supplementary information about operating conditions and system kinematics is unavailable.

show abstract

Section: Dilated Cnnmentioning

confidence: 99%

Vibration Signal Decomposition using Dilated CNN

Gildish,

Grebshtein,

Aperstein

et al. 2023

PHM_CONF

View full text Add to dashboard Cite

show abstract

Three Novel Artificial Neural Network Architectures Based on Convolutional Neural Networks for the Spatio-Temporal Processing of Solar Forecasting Data

Benavides Cesar,

Manso-Callejo,

Cira

2024

Applied Sciences

View full text Add to dashboard Cite

In this work, three new convolutional neural network models—spatio-temporal convolutional neural network versions 1 and 2 (ST_CNN_v1 and ST_CNN_v2), and the spatio-temporal dilated convolutional neural network (ST_Dilated_CNN)—are proposed for solar forecasting and processing global horizontal irradiance (GHI) data enriched with meteorological and astronomical variables. A comparative analysis of the proposed models with two traditional benchmark models shows that the proposed ST_Dilated_CNN model outperforms the rest in capturing long-range dependencies, achieving a mean absolute error of 31.12 W/m2, a mean squared error of 54.07 W/m2, and a forecast skill of 37.21%. The statistical analysis carried out on the test set suggested highly significant differences in performance (p-values lower than 0.001 for all metrics in all the considered scenarios), with the model with the lowest variability in performance being ST_CNN_v2. The statistical tests applied confirmed the robustness and reliability of the proposed models under different conditions. In addition, this work highlights the significant influence of astronomical variables on prediction performance. The study also highlights the intricate relationship between the proposed models and meteorological and astronomical input characteristics, providing important insights into the field of solar prediction and reaffirming the need for further research into variability factors that affect the performance of models.

show abstract

A Dilated Convolutional Based Model for Time Series Forecasting

Cited by 2 publications

References 23 publications

Vibration Signal Decomposition using Dilated CNN

Vibration Signal Decomposition using Dilated CNN

Three Novel Artificial Neural Network Architectures Based on Convolutional Neural Networks for the Spatio-Temporal Processing of Solar Forecasting Data

Contact Info

Product

Resources

About