An Approach to Time Series Classification Using Binary Distribution Tree

Ma, Chao; Shi, Xiaochuan; Zhu, Wei-Ping; Li, Wei; Cui, Xiaohui; Gui, Hao

doi:10.1109/msn48538.2019.00082

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…In the process of model optimization, they apply a gradient descent with GA to improve performance. Their experimental results align with previous studies [24,25].…”

Section: Price Forecasting Using Machine Learning Methodssupporting

confidence: 91%

A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Piao

Zhao

et al. 2021

Agriculture

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Forecasting the prices of hogs has always been a popular field of research. Such information has played an essential role in decision-making for farmers, consumers, corporations, and governments. It is hard to predict hog prices because too many factors can influence them. Some of the factors are easy to quantify, but some are not. Capturing the characteristics behind the price data is also tricky considering their non-linear and non-stationary nature. To address these difficulties, we propose Heterogeneous Graph-enhanced LSTM (HGLTSM), which is a method that predicts weekly hog price. In this paper, we first extract the historical prices of necessary agricultural products in recent years. Then, we utilize discussions from the online professional community to build heterogeneous graphs. These graphs have rich information of both discussions and the engaged users. Finally, we construct HGLSTM to make the prediction. The experimental results demonstrate that forum discussions are beneficial to hog price prediction. Moreover, our method exhibits a better performance than existing methods.

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“…In the process of model optimization, they apply a gradient descent with GA to improve performance. Their experimental results align with previous studies [24,25].…”

Section: Price Forecasting Using Machine Learning Methodssupporting

confidence: 91%

A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Piao

Zhao

et al. 2021

Agriculture

Self Cite

View full text Add to dashboard Cite

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“…Methods based on deep learning have been investigated to resolve the limitations of manual feature engineering [13,14]. The text is automatically mapped to a low-dimensional vector through the model to extract text features.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Bert-Enhanced Text Graph Neural Network for Classification

Yang

Cui

2021

Entropy

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Text classification is a fundamental research direction, aims to assign tags to text units. Recently, graph neural networks (GNN) have exhibited some excellent properties in textual information processing. Furthermore, the pre-trained language model also realized promising effects in many tasks. However, many text processing methods cannot model a single text unit’s structure or ignore the semantic features. To solve these problems and comprehensively utilize the text’s structure information and semantic information, we propose a Bert-Enhanced text Graph Neural Network model (BEGNN). For each text, we construct a text graph separately according to the co-occurrence relationship of words and use GNN to extract text features. Moreover, we employ Bert to extract semantic features. The former part can take into account the structural information, and the latter can focus on modeling the semantic information. Finally, we interact and aggregate these two features of different granularity to get a more effective representation. Experiments on standard datasets demonstrate the effectiveness of BEGNN.

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“…As with the experimental settings of other baseline algorithms, [8][9][10]14,15 we conduct experiments using the same experimental architecture for all datasets, and the network architectures only differ in the number of output categories for different datasets. Table 1 shows the statistical description of the 46 data sets used in our study, with the shortest time series feature length of 60 and the longest of 2709, with an average length of 492.96.…”

Section: Methodsmentioning

confidence: 99%

An algorithm to improve the performance of convolutional neural networks for TSC tasks

Liu

2022

Engineering Reports

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The convolutional neural network (CNN) is an important network model for processing time series classification tasks. However, there can be a lot of noise in the data itself. The clarity of spatial feature expression of time‐series samples and the freeness and balance of data samples both have a big effect on how well CNNs can handle time series classification (TSC) tasks. This paper proposes a multi‐pose marking learning algorithm (MPML), which optimizes the spatial representation of data samples through multiple graphical representations of time series data and then solves the problems of freeness and heterogeneity of data samples through labeling and preferential learning of poorly trained sample data. The algorithm improves the model's robustness to noise by improving the spatial expression sentiment of the time series and the labeling learning of the feature data, thereby improving the convolutional neural network's performance to perform the TSC task. We show how well the algorithm works by testing it on 46 datasets from the UCR Time Series Classification Archive and comparing the results.

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An Approach to Time Series Classification Using Binary Distribution Tree

Cited by 9 publications

References 15 publications

A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Bert-Enhanced Text Graph Neural Network for Classification

An algorithm to improve the performance of convolutional neural networks for TSC tasks

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