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Zhenwen He, Shirong Long, Xiaogang Ma and Hong Zhao
A large amount of time series data is being generated every day in a wide range of sensor application domains. The symbolic aggregate approximation (SAX) is a well-known time series representation method, which has a lower bound to Euclidean distance and...
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Yubo Zheng, Yingying Luo, Hengyi Shao, Lin Zhang and Lei Li
Contrastive learning, as an unsupervised technique, has emerged as a prominent method in time series representation learning tasks, serving as a viable solution to the scarcity of annotated data. However, the application of data augmentation methods duri...
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Rahul Agrahari, Matthew Nicholson, Clare Conran, Haytham Assem and John D. Kelleher
In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly d...
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Zhenwen He, Chunfeng Zhang, Xiaogang Ma and Gang Liu
Time series data are widely found in finance, health, environmental, social, mobile and other fields. A large amount of time series data has been produced due to the general use of smartphones, various sensors, RFID and other internet devices. How a time...
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Kiburm Song, Minho Ryu and Kichun Lee
Numerous dimensionality-reducing representations of time series have been proposed in data mining and have proved to be useful, especially in handling a high volume of time series data. Among them, widely used symbolic representations such as symbolic ag...
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Zhenwen He, Chi Zhang and Yunhui Cheng
Time series data typically exhibit high dimensionality and complexity, necessitating the use of specific approximation methods to perform computations on the data. The currently employed compression methods suffer from varying degrees of feature loss, le...
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Fuyu Xu and Kate Beard
Measures of similarity or differences between data objects are applied frequently in geography, biology, computer science, linguistics, logic, business analytics, and statistics, among other fields. This work focuses on event sequence similarity among ev...
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Adriano Mancini, Francesco Solfanelli, Luca Coviello, Francesco Maria Martini, Serena Mandolesi and Raffaele Zanoli
Yield prediction is a crucial activity in scheduling agronomic operations and in informing the management and financial decisions of a wide range of stakeholders of the organic durum wheat supply chain. This research aims to develop a yield forecasting s...
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Shlomo Dubnov
Capturing long-term statistics of signals and time series is important for modeling recurrent phenomena, especially when such recurrences are a-periodic and can be characterized by the approximate repetition of variable length motifs, such as patterns in...
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Pavel Ma?karinec
This paper focusses on the analysis of female political participation in the decision-making processes at the local level. We analyse women?s descriptive representation in Czechia on a very detailed spatial structure and an extended yearly time series (1...
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FRANCISCO ESTRADA,VÍCTOR M. GUERRERO
Pág. 429 - 449
This paper proposes a new methodology for generating climate change scenarios at the local scale based on multivariate time series models and restricted forecasting techniques. This methodology offers considerable advantages over the current statistical ...
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Mingze Li, Bing Li, Zhigang Qi, Jiashuai Li and Jiawei Wu
Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emer...
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Hui Sheng, Min Liu, Jiyong Hu, Ping Li, Yali Peng and Yugen Yi
Time-series data is an appealing study topic in data mining and has a broad range of applications. Many approaches have been employed to handle time series classification (TSC) challenges with promising results, among which deep neural network methods ha...
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Eoin Cartwright, Martin Crane and Heather J. Ruskin
As the availability of big data-sets becomes more widespread so the importance of motif (or repeated pattern) identification and analysis increases. To date, the majority of motif identification algorithms that permit flexibility of sub-sequence length d...
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Maksim Godovykh, Jorge Ridderstaat, Carissa Baker and Alan Fyall
COVID-19 has significantly influenced tourism, including tourists? and residents? attitudes toward tourism. At the same time, attitudes and consumer confidence are important for economic recovery in the tourism sector. This study explores the effects of ...
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Manuel Lopez-Martin, Antonio Sanchez-Esguevillas, Luis Hernandez-Callejo, Juan Ignacio Arribas and Belen Carro
This work brings together and applies a large representation of the most novel forecasting techniques, with origins and applications in other fields, to the short-term electric load forecasting problem. We present a comparison study between different cla...
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Ching-Min Chang, Kuo-Chen Ma and Mo-Hsiung Chuang
Predicting the effects of changes in dissolved input concentration on the variability of discharge concentration at the outlet of the catchment is essential to improve our ability to address the problem of surface water quality. The goal of this study is...
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Chulseung Yang, Gi Won Ku, Jeong-Gi Lee and Sang-Hyun Lee
This paper presents an interval-based LDA (Linear Discriminant Analysis) algorithm for individual verification using ECG (Electrocardiogram). In this algorithm, at first, unwanted noise and power-line interference are removed from the ECG signal. Then, t...
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Ba-Huy Tran, Nathalie Aussenac-Gilles, Catherine Comparot and Cassia Trojahn
Semantic technologies are at the core of Earth Observation (EO) data integration, by providing an infrastructure based on RDF representation and ontologies. Because many EO data come in raster files, this paper addresses the integration of data calculate...
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Lorenzo Gianquintieri, Maria Antonia Brovelli, Andrea Pagliosa, Gabriele Dassi, Piero Maria Brambilla, Rodolfo Bonora, Giuseppe Maria Sechi and Enrico Gianluca Caiani
The epidemic of coronavirus-disease-2019 (COVID-19) started in Italy with the first official diagnosis on 21 February 2020; However, it is not known how many cases were already present in earlier days and weeks, thus limiting the possibilities of conduct...
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