Using multiple time series analysis for geosensor data forecasting

Pravilovic, Sonja; Bilancia, Massimo; Appice, Annalisa; Malerba, Donato

doi:10.1016/j.ins.2016.11.001

Cited by 35 publications

(16 citation statements)

References 39 publications

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“…Schuster et al [20] proposed adopting intra-annual time series analysis in the comparative application of pasture habitats to explore technologies for comparison of the satellite data of RapidEye and TerraSAR-X. Pravilovic et al [21] presented a cluster-centric forecasting methodology that allows users to yield a characterization of correlation in geophysical time series through a spatio-temporal clustering step, and the experimental results highlight the importance of dealing with both temporal and spatial correlation, and validate the proposed cluster-centric strategy in the computation of a multivariate time series forecasting model. Shrestha and Bhatta [22] discussed the properties of time series data, compared common data analysis methods, and presented a methodological framework with an example: Nepal's money-price relationship for time series data analysis, and the test results obtained following this methodological framework were found to be more robust and reliable.…”

Section: Domestic and Foreign Literature Analysis And Development Tecmentioning

confidence: 99%

A Big Data and Time Series Analysis Technology-Based Multi-Agent System for Smart Tourism

Chen

Yang

2018

Applied Sciences

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This study focuses on presenting a development trend from the perspective of data-oriented evidence, especially open data and technologies, as those numbers can verify and prove current technology trends and user information requirements. According to the practical progress of Dr. What-Info I and II, this paper continues to develop Dr. What-Info III. Moreover, big data technology, the MapReduce paralleled decrement mechanism of the cloud information agent CEOntoIAS, which is supported by a Hadoop-like framework, Software R, and time series analysis are adopted to enhance the precision, reliability, and integrity of cloud information. Furthermore, the proposed system app receives a collective satisfaction score of 80% in terms of Quesenbery's 5Es and Nielsen ratings. In addition, the verification results of the interface design show that the human-machine interface of our proposed system can meet important design preferences and provide approximately optimal balance. The top-n experiment shows that the top-5 recommendations would be better for solving the traditional tradeoff between output quality and processing time. Finally, the system effectiveness experiments indicate that the proposed system receives an overall up-to-standard function rate of 87.5%, and such recommendations provide this system with high information correctness and user satisfaction. Although there is plenty of room for improvement in experience, the feasibility of this service architecture has been proven.

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Section: Domestic and Foreign Literature Analysis And Development Tecmentioning

confidence: 99%

A Big Data and Time Series Analysis Technology-Based Multi-Agent System for Smart Tourism

Chen

Yang

2018

Applied Sciences

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“…For network anomaly detection, time series are aggregated into difference resolutions and wavelet based methods such as change point detection or time series outlier detection are used across resolutions [24]. [11] presents a cluster-centric forecasting methodology using data aggregated at multiple spatial/spatio-temporal resolutions. To the best of our knowledge, our work is the first to build a predictive model for M 3 R time series data.…”

Section: Time Series Analysismentioning

confidence: 99%

Learning from Multi-Modality Multi-Resolution Data: an Optimization Approach

Zhu¹,

2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Many complex real applications involve the collection of time series data with multiple modalities and of multiple resolutions. For example, in aluminum smelting processes, the recorded process variables typically reflect various aspects of these processes, such as pressure and temperature, and they are often obtained with different time resolutions, such as every 5 minutes and every day. How can we effectively leverage both the multi-modality property and the multi-resolution property of the data for the sake of more accurate prediction of key process indicators (e.g., the cell temperature of the aluminum smelting processes)?Different from existing techniques, which can only model the multi-modality property or the multi-resolution property, in this paper, for the first time, we propose to jointly model the two properties such that the prediction results are consistent across multiple modalities and multiple resolutions. To this end, we construct an optimization framework, which is based on a novel regularizer imposing such consistency. Then, we design an effective and efficient optimization algorithm based on randomized block coordinate descent. Its performance is evaluated on both synthetic and real data sets, outperforming state-of-the-art techniques.

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“…Hence, the identification of influential nodes in complex networks play an important role [11] in both structural and functional aspects [12,13], and is an important area of research [14]. The identification of influential nodes can be applied across various fields [15] such as disease [16], network system [17], biology [18], social system [19,20,4], time series [21], information propagation [22] and Parrondo's paradox [23,24,25,26,27]. Besides, identifying the vital nodes [28] can allow us to discover and address real-world problems [29,30] such as transportation hubs identifying, influence maximizing, rumor controlling [31], disease controlling [32], advertising and community finding [33,34].…”

Section: Introductionmentioning

confidence: 99%

Identifying Influential Nodes in Complex Networks Based on Local Effective Distance

et al. 2019

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With the rapid development of Internet technology, the social network has gradually become an indispensable platform for users to release information, obtain information, and share information. Users are not only receivers of information, but also publishers and disseminators of information. How to select a certain number of users to use their influence to achieve the maximum dissemination of information has become a hot topic at home and abroad. Rapid and accurate identification of influential nodes in the network is of great practical significance, such as the rapid dissemination, suppression of social network information, and the smooth operation of the network. Therefore, from the perspective of improving computational accuracy and efficiency, we propose an influential node identification method based on effective distance, named KDEC. By quantifying the effective distance between nodes and combining the position of the node in the network and its local structure, the influence of the node in the network is obtained, which is used as an indicator to evaluate the influence of the node. Through experimental analysis of a lot of real-world networks, the results show that the method can quickly and accurately identify the influential nodes in the network, and is better than some classical algorithms and some recently proposed algorithms.

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Using multiple time series analysis for geosensor data forecasting

Cited by 35 publications

References 39 publications

A Big Data and Time Series Analysis Technology-Based Multi-Agent System for Smart Tourism

A Big Data and Time Series Analysis Technology-Based Multi-Agent System for Smart Tourism

Learning from Multi-Modality Multi-Resolution Data: an Optimization Approach

Identifying Influential Nodes in Complex Networks Based on Local Effective Distance

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