Event pattern discovery by keywords in graph streams

Namaki, Mohammad Hossein; Lin, Peng; Wu, Yinghui

doi:10.1109/bigdata.2017.8258019

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…The studies related to time series prediction through deep neural network mainly have three categories. One is to identify statistically significant events in time series (Chau & Wong, 1999;Liu & Yue, 2018;Malhotra, Vig, Shroff, & Agarwal, 2015;Namaki, Lin, & Wu, 2017). The second one is to seek and predict inherent structure in the time series.…”

Section: Literature Reviewmentioning

confidence: 99%

Agricultural commodity futures prices prediction via long- and short-term time series network

Ouyang

Wei

2019

Journal of Applied Economics

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In this study, we attempt to predict global agricultural commodity futures prices through analysis of multivariate time series. Our motivation is based on the notion that datasets of agricultural commodity futures prices involves a mixture of long-and short-term information, linear and non-linear structure, for which traditional approaches such as Auto-Regressive Integrated Moving Average (ARIMA) and Vector Auto-Regression (VAR) may fail. To tackle this issue, Long-and Short-Term Time-series Network (LSTNet) is applied for prediction. Empirical results show that LSTNet achieves better performance over that of several state-of-the-art baseline methods on average and in most periods based on three performance evaluation measures and two tests of performance difference.

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Section: Literature Reviewmentioning

confidence: 99%

Agricultural commodity futures prices prediction via long- and short-term time series network

Ouyang

Wei

2019

Journal of Applied Economics

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“…Second, different works focus on solving a specific graph problem in a streaming setting. Targeted problems include graph clustering [103], mining periodic cliques [174], search for persistent communities [140], [176], tracking conductance [84], event pattern [166] and subgraph [162] discovery, solving ego-centric queries [161], pattern detection [53], [85], [186], [131], [141], [194], [54], [86], densest subgraph identification [113], frequent subgraph mining [19], dense subgraph detection [145], construction and querying of knowledge graphs [52], stream summarization [92], graph sparsification [11], [25], k-core maintenance [13], shortest paths [193], Betweenness Centrality [104], [199], [192], Triangle Counting [147], Katz Centrality [203], mincuts [133], [89] Connected Components [151], or PageRank [97], [55].…”

Section: Specific Streaming Solutionsmentioning

confidence: 99%

Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems

Besta¹,

Fischer²,

Kalavri³

et al. 2019

Preprint

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Graph processing has become an important part of various areas of computing, including machine learning, medical applications, social network analysis, computational sciences, and others. A growing amount of the associated graph processing workloads are dynamic, with millions of edges added or removed per second. Graph streaming frameworks are specifically crafted to enable the processing of such highly dynamic workloads. Recent years have seen the development of many such frameworks. However, they differ in their general architectures (with key details such as the support for the parallel execution of graph updates, or the incorporated graph data organization), the types of updates and workloads allowed, and many others. To facilitate the understanding of this growing field, we provide the first analysis and taxonomy of dynamic and streaming graph processing. We focus on identifying the fundamental system designs and on understanding their support for concurrency and parallelism, and for different graph updates as well as analytics workloads. We also crystallize the meaning of different concepts associated with streaming graph processing, such as dynamic, temporal, online, and time-evolving graphs, edge-centric processing, models for the maintenance of updates, and graph databases. Moreover, we provide a bridge with the very rich landscape of graph streaming theory by giving a broad overview of recent theoretical related advances, and by analyzing which graph streaming models and settings could be helpful in developing more powerful streaming frameworks and designs. We also outline graph streaming workloads and research challenges.

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“…During the recent years, research papers have discussed the promising tools in time series forecasting provided by deep neural networks that can be classified into three categories [14]. First category is to identify statistically significant events, second is to find and predict inherent structure and third is to do accurate prediction on numerical value [15]. Looking into time series prediction through deep neural networks, there are several famous approaches which include Long Short Term Memory (LSTM) and Support Vector Machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques

Chen¹,

Goh²,

Sin³

et al. 2021

Adv. sci. technol. eng. syst. j.

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The intention of this research is to study and design an automated agriculture commodity price prediction system with novel machine learning techniques. Due to the increasing large amounts historical data of agricultural commodity prices and the need of performing accurate prediction of price fluctuations, the solution has largely shifted from statistical methods to machine learning area. However, the selection of proper machine learning techniques for automated agriculture commodity price prediction still has limited consideration. On the other hand, when implementing machine learning techniques, finding a suitable model with optimal parameters for global solution, nonlinearity and avoiding curse of dimensionality are still biggest challenges. In this research, we address these problems by conducting a machine learning strategy study and propose a web-based automated system to predict agriculture commodity price. In the two series experiments, five popular machine learning algorithms, ARIMA, SVR, Prophet, XGBoost and LSTM have been compared with large historical datasets in Malaysia. The results validate the efficiency of the proposed Long Short-Term Memory Model (LSTM) to serve as the prediction engine for the proposed system. Particularly in the long-term experiment testing, the average performance of LSTM with MSE has improved 45.5% while ARIMA has dropped 74.1% and the average MSE of LSTM is 0.304 which outperformed all other four algorithms.

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Event pattern discovery by keywords in graph streams

Cited by 11 publications

References 14 publications

Agricultural commodity futures prices prediction via long- and short-term time series network

Agricultural commodity futures prices prediction via long- and short-term time series network

Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems

Automated Agriculture Commodity Price Prediction System with Machine Learning Techniques

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