Temporal Multiple-convolutional Network for Commodity Classification of Online Retail Platform Data

A deep learning framework for constitutive modeling based on temporal convolutional network

Accurate short-term GHI forecasting using a novel temporal convolutional network model

Veg-W2TCN: A parallel hybrid forecasting framework for non-stationary time series using wavelet and temporal convolution network model

Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization algorithm

Accurate and reliable estimation of reference crop evapotranspiration (ET0) in the North Henan Plain is crucial for agricultural water resource management, production, and food supply in China. This study aims to evaluate the performance of deep learning (DL) methods in ET0 estimation and assess the applicability of the developed DL model beyond the training domain. This study utilized historical meteorological data from Zhengzhou City, northern Henan, spanning 2010–2024. Meteorological variables were selected through correlation analysis and maximum information coefficient (MIC). A novel DL model—the TCN-Attention model (TA)—was constructed by incorporating a self-attention mechanism into the temporal convolutional network (TCN) model. This model was compared with two classical DL models—Long Short-Term Memory (LSTM) and TCN. Results indicate: (1) Sunshine duration (n), relative humidity (RH), and maximum temperature (Tmax) are the three most significant features influencing summer maize evapotranspiration; (2) prediction accuracy under the same input scenarios: TA model > TCN model > LSTM model; (3) in scenarios where only temperature data is input, the TA model has the highest prediction accuracy, surpassing the H-S empirical method; and (4) for limited meteorological data, the combination of temperature and humidity was found to be most effective, showing good adaptability and accuracy at different time steps (hourly: R2 = 0.982; daily: R2 = 0.975; weekly: R2 = 0.928). This study highlights the potential of the TA model for estimating reference crop evapotranspiration in the northern Henan Plain, which may provide theoretical guidance for crop irrigation management under future climate change.

SummaryThe usage of multi‐rotor unmanned aerial vehicles (UAVs) has rapidly increased in a variety of industries recently, which has caused a quick rise in the quantity of research works on the topic. Remaining battery life prediction, anomaly detection and instantaneous power consumption prediction are among the topics that attract the most attention of researchers. This article presents the development and utilization of a modified temporal convolutional network (TCN) model, a commonly employed approach for anomaly detection and instantaneous power consumption prediction in unmanned aerial vehicles (UAVs). The first modification to the TCN model was to use the Leaky ReLu activation function in place of the rectified linear unit (ReLu) activation function from the original TCN model. In the next step, instantaneous power consumption prediction for UAVs was performed by using the data obtained from both sensors and simple moving average (SMA) algorithm. As a result of the tests performed with the created simulation setup, it has been clearly shown that the proposed method gives better results compared to other deep learning models used for comparison with the lowest RMSE of 0.0496.

The accurate and rapid prediction of parking availability is helpful for improving parking efficiency and to optimize traffic systems. However, previous studies have suffered from limited training sample sizes and a lack of thorough investigation into the correlations among the factors affecting parking availability. The purpose of this study is to explore a prediction method that can account for multiple factors. Firstly, a dynamic prediction method based on a temporal convolutional network (TCN) model was confirmed to be efficient for ultra-short-term parking availability with an accuracy of 0.96 MSE. Then, an attention-enhanced TCN (A-TCN) model based on spatial attention modules was proposed. This model integrates multiple factors, including related dates, extreme weather, and human control, to predict the daily congestion index of parking lots in the short term, with a prediction period of up to one month. Experimental results on real data demonstrate that the MSE of A-TCN is 0.0061, exhibiting better training efficiency and prediction accuracy than a traditional TCN for the short-term prediction time scale.

Maritime vessel movement prediction: A temporal convolutional network model with optimal look-back window size determination

TCN-based M-ary mobile spread spectrum underwater acoustic communication

Motor imagery (MI) is a cognitive process wherein an individual mentally rehearses a specific movement without physically executing it. Recently, MI-based brain-computer interface (BCI) has attracted widespread attention. However, accurate decoding of MI and understanding of neural mechanisms still face huge challenges. These seriously hinder the clinical application and development of BCI systems based on MI. Thus, it is very necessary to develop new methods to decode MI tasks. In this work, we propose a multi-branch convolutional neural network (MBCNN) with a temporal convolutional network (TCN), an end-to-end deep learning framework to decode multi-class MI tasks. We first used MBCNN to capture the MI electroencephalography signals information on temporal and spectral domains through different convolutional kernels. Then, we introduce TCN to extract more discriminative features. The within-subject cross-session strategy is used to validate the classification performance on the dataset of BCI Competition IV-2a. The results showed that we achieved 75.08% average accuracy for 4-class MI task classification, outperforming several state-of-the-art approaches. The proposed MBCNN-TCN-Net framework successfully captures discriminative features and decodes MI tasks effectively, improving the performance of MI-BCIs. Our findings could provide significant potential for improving the clinical application and development of MI-based BCI systems.

Monaural Speech Enhancement Using a Multi-Branch Temporal Convolutional Network

A unified modeling framework for lane change intention recognition and vehicle status prediction

The widespread use of the Unmanned Aerial Vehicles (UAVs) in many fields of industry such as defense, entertainment and transportation in recent years has led researchers to carry out many studies on UAV controls. One of the most popular of these study topics is the prediction of the instantaneous power consumption of UAVs during flight. In this study, a modified version of the Temporal Convolutional Network (TCN) architecture, which has recently been widely used for time series prediction, is used for prediction the UAVs' power consumption. As a result of the simulation processes performed using the flight data obtained from the DJI Matrice M100 drone, it was observed that the modified TCN model, which has an RMSE value of 0.0635, achieves better predictions compared to the recurrent neural networks and the classical TCN model.

Sleep staging is an important means of diagnosing sleep disorders and monitoring sleep quality. Concurrently, the RNN (Recurrent Neural Network) models commonly used in this field limits the overall calculation efficiency and performance. Specifically, we utilize the TCN (Temporal Convolutional Network) model advanced in the audio field as an alternative for RNN models to improve temporal information collection capability. Leverage the SE (squeeze-and-extraction) module to merge the multi-lead information flexibly. Implement the feature extraction layer by the design of CNN to reduce computational complexity. The proposed model, named STCN, achieved 85.01% accuracy, 78.80% F1 value on the Sleep-EDFx dataset, and achieved 84.10% accuracy, 81.27% F1 value on the Physionet 2018 dataset. Only 1.6 million parameters are obtained in STCN, which is significantly smaller than the parameters of Deepsleepnet. The proposed model is compatible with scenarios from different datasets, achieves an adaptive fusion of each lead information and supplies a more reliable prediction for sleep quality assessment to provide a foundation for convenient applications in the future.

Rainfall–runoff modeling is a complex nonlinear time-series problem in the field of hydrology. Various methods, such as physical-driven and data-driven models, have been developed to study the highly random rainfall–runoff process. In the past 2 years, with the advancement of computing hardware resources and algorithms, deep-learning methods, such as temporal convolutional network (TCN), have been shown to be good prospects in time-series prediction tasks. The aim of this study is to develop a prediction model based on TCN structure to simulate the hourly rainfall–runoff relationship. We use two datasets in the Jingle and Kuye watersheds to test the model under different network structures and compare with the other four models. The results show that the TCN model outperforms the Excess Infiltration and Excess Storage Model (EIESM), artificial neural network, and long short-term memory and improves the flood forecasting accuracy at different foreseeable periods. It is shown that the TCN has a faster convergence rate and is an effective method for hydrological forecasting.