Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in t...
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Time series forecasting plays an important role in various fields, such as energy, finance, transport, and weather. Temporal convolutional networks (TCNs) based on dilated causal convolution have been widely used in time series forecasting. However, two problems weaken the performance of TCNs. One is that in dilated casual convolution, causal convolution leads to the receptive fields of outputs being concentrated in the earlier part of the input sequence, whereas the recent input information will be severely lost. The other is that the distribution shift problem in time series has not been adequately solved. To address the first problem, we propose a subsequence-based dilated convolution method (SDC). By using multiple convolutional filters to convolve elements of neighboring subsequences, the method extracts temporal features from a growing receptive field via a growing subsequence rather than a single element. Ultimately, the receptive field of each output element can cover the whole input sequence. To address the second problem, we propose a difference and compensation method (DCM). The method reduces the discrepancies between and within the input sequences by difference operations and then compensates the outputs for the information lost due to difference operations. Based on SDC and DCM, we further construct a temporal subsequence-based convolutional network with difference (TSCND) for time series forecasting. The experimental results show that TSCND can reduce prediction mean squared error by 7.3% and save runtime, compared with state-of-the-art models and vanilla TCN.
The integration of the Internet of Things (IoT) with mobile edge computing (MEC) has come out to be a promising solution to address the requirements of high computing capabilities and low latency services, enabling us...
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Video analysis typically requires a significant amount of computing resources and energy. Traditional cloud-based video analysis relies on concentrating computing resources in the cloud, which puts a tremendous load o...
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Due to their fast speed and easy deployment, Unmanned Aerial Vehicles (UAVs) have been widely used across various sectors, such as earthquake rescue, medical assistance, and smart agriculture. However, UAVs in deliver...
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The real-time query of surveillance video plays a significant role in many fields such as public safety, smart city, and abnormality monitoring. However, with the exponential growth of surveillance video data, traditi...
The power line is a crucial component of the power system. However, long-term exposure to the natural environment can lead to various defects, such as burrs, corrosion, and corona. These defects seriously threaten the...
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Due to the harsh underwater communication environment, the design of unique routing protocols is essential for underwater sensor networks. This paper proposes a Hybrid Forwarding Strategy Pressure-Based Routing (HFS-P...
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In this paper, we propose a localization algorithm framework for wireless sensor networks (WSNs) based on cluster implementation. The algorithm addresses the issue of information locality in traditional distributed lo...
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A Programmable Logic controller (PLC) is an essentially domain-specific computer used to control physical equipment and is widely used in industrial control fields. It plays a crucial role in automating complex proces...
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Human Action Recognition (HAR) has received widespread attention in recent years. For skeleton modality, the representation of spatio-temporal motion and the weight allocation of different streams are still under disc...
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