Results 11 to 20 of about 655,816 (301)
A Multiresolution Symbolic Representation of Time Series [PDF]
21st International Conference on Data Engineering (ICDE'05), 2005 Efficiently and accurately searching for similarities among time series and discovering interesting patterns is an important and non-trivial problem. In this paper, we introduce a new representation of time series, the Multiresolution Vector Quantized (MVQ) approximation, along with a new distance function.
Vasileios Megalooikonomou +3 more
core +4 more sources
Variable-Size Segmentation for Time Series Representation
, 2023 Given the high data volumes in time series applications, or simply the need for fast response times, it is usually necessary to rely on alternative, shorter representations of time series, usually with information loss. This incurs approximate comparisons of time series where precision is a major issue.
Djebour, Lamia +2 more
openaire +3 more sources
Time Series Representation Models
CoRRTime series analysis remains a major challenge due to its sparse characteristics, high dimensionality, and inconsistent data quality. Recent advancements in transformer-based techniques have enhanced capabilities in forecasting and imputation; however, these methods are still resource-heavy, lack adaptability, and face difficulties in integrating both ...
Robert Leppich +3 more
openaire +3 more sources
TS2Vec: Towards Universal Representation of Time Series
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 This paper presents TS2Vec, a universal framework for learning representations of time series in an arbitrary semantic level. Unlike existing methods, TS2Vec performs contrastive learning in a hierarchical way over augmented context views, which enables a robust contextual representation for each timestamp.
Zhihan Yue +6 more
openaire +3 more sources
Visualising deep network time-series representations
Neural Computing and Applications, 2021 Despite the popularisation of machine learning models, more often than not, they still operate as black boxes with no insight into what is happening inside the model. There exist a few methods that allow to visualise and explain why a model has made a certain prediction.
Blazej Leporowski, Alexandros Iosifidis
openaire +2 more sources
Space-efficient representations of raster time series
Information Sciences, 2021 Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG [Abstract] Raster time series, a.k.a. temporal rasters, are collections of rasters covering the same region at consecutive timestamps. These data have been used in many different applications ranging from weather forecast systems to monitoring of forest degradation or soil ...
Fernando Silva-Coira +3 more
openaire +2 more sources
Survey on Feature Representation and Similarity Measurement of Time Series
Jisuanji kexue yu tansuo, 2021 Time series is a group of random numbers which are composed of the values of the same index according to the time sequence. With the rapid development of science and technology, the application of time series in the field of data mining becomes more and ...
SUN Dongpu, QU Li
doaj +1 more source
A Compact Representation of Raster Time Series [PDF]
2019 Data Compression Conference (DCC), 2019 The raster model is widely used in Geographic Information Systems to represent data that vary continuously in space, such as temperatures, precipitations, elevation, among other spatial attributes. In applications like weather forecast systems, not just a single raster, but a sequence of rasters covering the same region at different timestamps, known ...
Cruces, Nataly +2 more
openaire +2 more sources
Time-series representation of the changes in topic dominance over time.
, 2022 Time-series representation of the changes in topic dominance over time.
Alexander Bogdanowicz (12457975) +1 more
core +1 more source
Transitional SAX Representation for Knowledge Discovery for Time Series
Applied Sciences, 2020 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.
Kiburm Song, Minho Ryu, Kichun Lee
doaj +1 more source