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Articoli di riviste sul tema "Time series search"

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Autore: Grafiati

Pubblicato: 8 giugno 2024

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1

Folgado, Duarte, Marília Barandas, Margarida Antunes, Maria Lua Nunes, Hui Liu, Yale Hartmann, Tanja Schultz e Hugo Gamboa. "TSSEARCH: Time Series Subsequence Search Library". SoftwareX 18 (giugno 2022): 101049. http://dx.doi.org/10.1016/j.softx.2022.101049.

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2

Luu, Do Ngoc, Nguyen Ngoc Phien e Duong Tuan Anh. "Tuning Parameters in Deep Belief Networks for Time Series Prediction through Harmony Search". International Journal of Machine Learning and Computing 11, n. 4 (agosto 2021): 274–80. http://dx.doi.org/10.18178/ijmlc.2021.11.4.1047.

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Abstract (sommario):

There have been several researches of applying Deep Belief Networks (DBNs) to predict time series data. Most of these works pointed out that DBNs can bring out better prediction accuracy than traditional Artificial Neural Networks. However, one of the main shortcomings of using DBNs in time series prediction concerns with the proper selection of their parameters. In this paper, we investigate the use of Harmony Search algorithm for determining the parameters of DBN in forecasting time series. Experimental results on several synthetic and real world time series datasets revealed that the DBN with parameters selected by Harmony Search performs better than the DBN with parameters selected by Particle Swarm Optimization (PSO) or random method in most of the tested datasets.

3

PRATT, KEVIN B., e EUGENE FINK. "SEARCH FOR PATTERNS IN COMPRESSED TIME SERIES". International Journal of Image and Graphics 02, n. 01 (gennaio 2002): 89–106. http://dx.doi.org/10.1142/s0219467802000482.

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Abstract (sommario):

We describe a technique for fast compression of time series, indexing of compressed series, and retrieval of series similar to a given pattern. The compression procedure identifies "important" points of a series and discards the other points. We use the important points not only for compression, but also for indexing a database of time series. Experiments show the effectiveness of this technique for indexing of stock prices, weather data and electroencephalograms.

4

SHIN, MIN-SU, e YONG-IK BYUN. "EFFICIENT PERIOD SEARCH FOR TIME SERIES PHOTOMETRY". Journal of The Korean Astronomical Society 37, n. 2 (1 giugno 2004): 79–85. http://dx.doi.org/10.5303/jkas.2004.37.2.079.

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5

Ibrahim, Ibrahim A., e Abdullah M. Albarrak. "Correlation-based search for time series data". International Journal of Computer Applications in Technology 62, n. 2 (2020): 158. http://dx.doi.org/10.1504/ijcat.2020.10026419.

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Ibrahim, A., e Abdullah M. Albarrak. "Correlation-based search for time series data". International Journal of Computer Applications in Technology 62, n. 2 (2020): 158. http://dx.doi.org/10.1504/ijcat.2020.104684.

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7

Luo, Wei, Marcus Gallagher e Janet Wiles. "Parameter-Free Search of Time-Series Discord". Journal of Computer Science and Technology 28, n. 2 (marzo 2013): 300–310. http://dx.doi.org/10.1007/s11390-013-1330-8.

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8

Huang, Silu, Erkang Zhu, Surajit Chaudhuri e Leonhard Spiegelberg. "T-Rex: Optimizing Pattern Search on Time Series". Proceedings of the ACM on Management of Data 1, n. 2 (13 giugno 2023): 1–26. http://dx.doi.org/10.1145/3589275.

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Abstract (sommario):

Pattern search is an important class of queries for time series data. Time series patterns often match variable-length segments with a large search space, thereby posing a significant performance challenge. The existing pattern search systems, for example, SQL query engines supporting MATCH_RECOGNIZE, are ineffective in pruning the large search space of variable-length segments. In many cases, the issue is due to the use of a restrictive query language modeled on time series points and a computational model that limits search space pruning. We built T-ReX to address this problem using two main building blocks: first, a MATCH_RECOGNIZE language extension that exposes the notion of segment variable and adds new operators, lending itself to better optimization; second, an executor capable of pruning the search space of matches and minimizing total query time using an optimizer. We conducted experiments using 5 real-world datasets and 11 query templates, including those from existing works. T-ReX outperformed an optimized NFA-based pattern search executor by 6x in median query time and an optimized tree-based executor by 19X.

9

Xiaoling WANG, e Clement H. C. LEUNG. "Representing Image Search Performance Using Time Series Models". International Journal of Advancements in Computing Technology 2, n. 4 (31 ottobre 2010): 140–50. http://dx.doi.org/10.4156/ijact.vol2.issue4.15.

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10

Liabotis, Ioannis, Babis Theodoulidis e Mohamad Saraaee. "Improving Similarity Search in Time Series Using Wavelets". International Journal of Data Warehousing and Mining 2, n. 2 (aprile 2006): 55–81. http://dx.doi.org/10.4018/jdwm.2006040103.

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11

Anupama Jawale e Ganesh Magar. "Time Series Similarity Search Methods for Sensor Data". Automatic Control and Computer Sciences 56, n. 2 (aprile 2022): 120–29. http://dx.doi.org/10.3103/s0146411622020067.

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12

Ma, Ruizhe, Diwei Zheng e Li Yan. "Fast Online Similarity Search for Uncertain Time Series". Journal of Computing and Information Technology 28, n. 1 (10 luglio 2020): 1–17. http://dx.doi.org/10.20532/cit.2020.1004574.

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Abstract (sommario):

To achieve fast retrieval of online data, it is needed for the retrieval algorithm to increase throughput while reducing latency. Based on the traditional online processing algorithm for time series data, we propose a spatial index structure that can be updated and searched quickly in a real-time environment. At the same time, we introduce an adaptive segmentation method to divide the space corresponding to nodes. Unlike traditional retrieval algorithms, for uncertain time series, the distance threshold used for screening will dynamically change due to noise during the search process. Extensive experiments are conducted to compare the accuracy of the query results and the timeliness of the algorithm. The results show that the index structure proposed in this paper has better efficiency while maintaining a similar true positive ratio.

13

Ospina-Holguín, Javier Humberto, e Ana Milena Padilla-Ospina. "THE SEARCH FOR TIME-SERIES PREDICTABILITY-BASED ANOMALIES". Journal of Business Economics and Management 23, n. 1 (29 novembre 2021): 1–19. http://dx.doi.org/10.3846/jbem.2021.15650.

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This paper introduces a new algorithm for exploiting time-series predictability-based patterns to obtain an abnormal return, or alpha, with respect to a given benchmark asset pricing model. The algorithm proposes a deterministic daily market timing strategy that decides between being fully invested in a risky asset or in a risk-free asset, with the trading rule represented by a parametric perceptron. The optimal parameters are sought in-sample via differential evolution to directly maximize the alpha. Successively using two modern asset pricing models and two different portfolio weighting schemes, the algorithm was able to discover an undocumented anomaly in the United States stock market cross-section, both out-of-sample and using small transaction costs. The new algorithm represents a simple and flexible alternative to technical analysis and forecast-based trading rules, neither of which necessarily maximizes the alpha. This new algorithm was inspired by recent insights into representing reinforcement learning as evolutionary computation.

14

Radha Devi D, Muruga, e Thambidurai P. "SIMILARITY SEARCH IN RECENT BIASED TIME SERIES DATABASES". International Journal on Information Sciences and Computing 5, n. 2 (2011): 37–46. http://dx.doi.org/10.18000/ijisac.50100.

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15

ZHOU, Da-zhuo, Xiao-li WU e Hong-can YAN. "An efficient similarity search for multivariate time series". Journal of Computer Applications 28, n. 10 (30 settembre 2009): 2541–43. http://dx.doi.org/10.3724/sp.j.1087.2008.02541.

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16

Xiang Lian e Lei Chen. "Efficient Similarity Search over Future Stream Time Series". IEEE Transactions on Knowledge and Data Engineering 20, n. 1 (gennaio 2008): 40–54. http://dx.doi.org/10.1109/tkde.2007.190666.

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17

Eravci, Bahaeddin, e Hakan Ferhatosmanoglu. "Diversity based relevance feedback for time series search". Proceedings of the VLDB Endowment 7, n. 2 (ottobre 2013): 109–20. http://dx.doi.org/10.14778/2732228.2732230.

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18

H Boersch-Supan, Philipp. "rucrdtw: Fast time series subsequence search in R". Journal of Open Source Software 1, n. 7 (7 novembre 2016): 100. http://dx.doi.org/10.21105/joss.00100.

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19

Hadj-Amar, Beniamino, Bärbel Finkenstädt Rand, Mark Fiecas, Francis Lévi e Robert Huckstepp. "Bayesian Model Search for Nonstationary Periodic Time Series". Journal of the American Statistical Association 115, n. 531 (9 luglio 2019): 1320–35. http://dx.doi.org/10.1080/01621459.2019.1623043.

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20

Li, Zhengxin, Jiansheng Guo, Hailin Li, Tao Wu, Sheng Mao e Feiping Nie. "Speed Up Similarity Search of Time Series Under Dynamic Time Warping". IEEE Access 7 (2019): 163644–53. http://dx.doi.org/10.1109/access.2019.2949838.

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21

McDonell, John R., e Don E. Waagen. "EVOLVING CASCADE-CORRELATION NETWORKS FOR TIME-SERIES FORECASTING". International Journal on Artificial Intelligence Tools 03, n. 03 (settembre 1994): 327–38. http://dx.doi.org/10.1142/s0218213094000169.

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Abstract (sommario):

This investigation applies evolutionary search to the cascade-correlation learning network. Evolutionary search is used to find both the input weights and input connectivity of candidate hidden units. A time-series prediction example is used to demonstrate the capabilities of the proposed approach.

22

KANG, SEONGGU, e SANGJUN LEE. "POLAR WAVELET TRANSFORM FOR TIME SERIES DATA". International Journal of Wavelets, Multiresolution and Information Processing 06, n. 06 (novembre 2008): 869–81. http://dx.doi.org/10.1142/s0219691308002720.

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Abstract (sommario):

In this paper, we propose the novel wavelet transform, called the Polar wavelet, which can improve the search performance in large time series databases. In general, Harr wavelet has been popularly used to extract features from time series data. However, Harr wavelet shows the poor performance for locally distributed time series data which are clustered around certain values, since it uses the averages to reduce the dimensionality of data. Moreover, Harr wavelet has the limitation that it works best if the length of time series is 2n, and otherwise it approximates the left side of real signal by substituting the right side with 0 elements to make the length of time series to 2n, which consequently, distortion of a signal occurs. The Polar wavelet does not only suggest the solution of the low distinction between time sequences of similar averages in Harr wavelet transform, but also improves the search performance as the length of time series is increased. Actually, several kinds of data such as rainfall are locally distributed and have the similar averages, so Harr wavelet which transforms data using their averages has shortcomings, naturally. To solve this problem, the Polar wavelet uses the polar coordinates which are not affected from averages and can improve the search performance especially in locally distributed time series databases. In addition, we show that the Polar wavelet guarantees no false dismissals. The effectiveness of the Polar wavelet is evaluated empirically on real weather data and the syntactic data, reporting the significant improvements in reducing the search space.

23

Duong, Anh Tuan. "AN OVERVIEW OF SIMILARITY SEARCH IN TIME SERIES DATA". Science and Technology Development Journal 14, n. 2 (30 giugno 2011): 71–79. http://dx.doi.org/10.32508/stdj.v14i2.1911.

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Abstract (sommario):

Time series data occur in many real life applications, ranging from science and engineering to business. In many of these applications, searching through large time series database based on query sequence is often desirable. Such similarity-based retrieval is also the basic subroutine in several advanced time series data mining tasks such as clustering, classification, finding motifs, detecting anomaly patterns, rule discovery and visualization. Although several different approaches have been developed, most are based on the common premise of dimensionality reduction and spatial access methods. This survey gives an overview of recent research and shows how the methods fit into a general framework of feature extraction.

24

Lee, Sang-Jun. "Efficient Similarity Search in Multi-attribute Time Series Databases". KIPS Transactions:PartD 14D, n. 7 (31 dicembre 2007): 727–32. http://dx.doi.org/10.3745/kipstd.2007.14-d.7.727.

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25

Kahveci, T., e A. K. Singh. "Optimizing similarity search for arbitrary length time series queries". IEEE Transactions on Knowledge and Data Engineering 16, n. 4 (aprile 2004): 418–33. http://dx.doi.org/10.1109/tkde.2004.1269667.

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26

Ding, Yiming, Wei Luo, Yufei Zhao, Zhen Li, Peng Zhan e Xueqing Li. "A Novel Similarity Search Approach for Streaming Time Series". Journal of Physics: Conference Series 1302 (agosto 2019): 022084. http://dx.doi.org/10.1088/1742-6596/1302/2/022084.

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27

KIM, S. W., J. KIM e S. PARK. "Physical Database Design for Efficient Time-Series Similarity Search". IEICE Transactions on Communications E91-B, n. 4 (1 aprile 2008): 1251–54. http://dx.doi.org/10.1093/ietcom/e91-b.4.1251.

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28

Martin, Y. R., A. W. Degeling e J. B. Lister. "Search for determinism in ELM time series in TCV". Plasma Physics and Controlled Fusion 44, n. 5A (30 aprile 2002): A373—A382. http://dx.doi.org/10.1088/0741-3335/44/5a/340.

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29

Mukhopadhyay, N. D., e S. Chatterjee. "Causality and pathway search in microarray time series experiment". Bioinformatics 23, n. 4 (8 dicembre 2006): 442–49. http://dx.doi.org/10.1093/bioinformatics/btl598.

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30

Xu, Yinfeng, Wenming Zhang e Feifeng Zheng. "Optimal algorithms for the online time series search problem". Theoretical Computer Science 412, n. 3 (gennaio 2011): 192–97. http://dx.doi.org/10.1016/j.tcs.2009.09.026.

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31

Stuhr, Andrew M., Eric D. Feigelson, Gabriel A. Caceres e Joel D. Hartman. "Autoregressive Planet Search: Feasibility Study for Irregular Time Series". Astronomical Journal 158, n. 2 (15 luglio 2019): 59. http://dx.doi.org/10.3847/1538-3881/ab26b3.

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32

Wensheng, Guo, e Ji Lianen. "Isomorphism Distance in Multidimensional Time Series and Similarity Search". Applied Mathematics & Information Sciences 7, n. 1L (1 febbraio 2013): 209–17. http://dx.doi.org/10.12785/amis/071l29.

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33

Avogadro, Paolo, Luca Palonca e Matteo Alessandro Dominoni. "Online anomaly search in time series: significant online discords". Knowledge and Information Systems 62, n. 8 (9 marzo 2020): 3083–106. http://dx.doi.org/10.1007/s10115-020-01453-4.

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34

Zhang, Wenming, Yinfeng Xu, Feifeng Zheng e Yucheng Dong. "Online algorithms for the multiple time series search problem". Computers & Operations Research 39, n. 5 (maggio 2012): 929–38. http://dx.doi.org/10.1016/j.cor.2011.07.011.

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35

Liu, Zheren, Chaogui Kang e Xiaoyue Xing. "Querying Similar Multi-Dimensional Time Series with a Spatial Database". ISPRS International Journal of Geo-Information 12, n. 4 (21 aprile 2023): 179. http://dx.doi.org/10.3390/ijgi12040179.

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Abstract (sommario):

Similar time series search is one of the most important time series mining tasks in our daily life. As recent advances in sensor technologies accumulate abundant multi-dimensional time series data associated with multivariate quantities, it becomes a privilege to adapt similar time series searches for large-scale and multi-dimensional time series data. However, traditional similar time series search methods are mainly designed for one-dimensional time series, while advanced methods applicable for multi-dimensional time series data are largely immature and, more importantly, are not friendly to users from the domain of geography. As an alternative, we propose a novel method to search similar multi-dimensional time series with spatial databases. Compared with traditional methods that often conduct the similarity search based on features of the raw time series data sequence, the proposed method stores multi-dimensional time series as spatial objects in a spatial database, and then searches similar time series based on their spatial features. To demonstrate the validity of the proposed method, we analyzed the correlation between temporal features of the raw time series and spatial features of their corresponding spatial objects theoretically and empirically. Results indicate that the proposed method can not only support similar multi-dimensional time series searches but also markedly improve its efficiency under many specific scenarios. We believe that such a new paradigm will shed further light on the similarity search in large-scale multi-dimensional time series data, and will lower the barrier for users familiar with spatial databases to conduct complex time series mining tasks.

36

Miller, Ryan, Harrison Schwarz e Ismael S. Talke. "Forecasting Sports Popularity: Application of Time Series Analysis". Academic Journal of Interdisciplinary Studies 6, n. 2 (26 luglio 2017): 75–82. http://dx.doi.org/10.1515/ajis-2017-0009.

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Abstract Popularity trends of the NFL and NBA are fun and interesting for casual fans while also of critical importance for advertisers and businesses with an interest in the sports leagues. Sports leagues have clear and distinct seasons and these have a major impact on when each league is most popular. To measure the popularity of each league, we used search data from Google Trends that gives real-time and historical data on the relative popularity of search words. By using search volume to measure popularity, the times of year, a sport is popular relative to its season can be explained. It is also possible to forecast how sport leagues are trending relative to each other. We compared and discussed three different univariate models both theoretically and empirically: the trend plus seasonality regression, Holt- Winters Multiplicative (HWMM), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models to determine the popularity trends. For each league, the six forecasting performance measures used in this study indicated HWMM gave the most accurate predictions.

37

Dai, Fang, e Gao Hua Liao. "Chaotic Time Series Adaptive Prediction Based on Volterra Series". Advanced Materials Research 945-949 (giugno 2014): 2495–98. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2495.

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At present, the mine has only realized the real-time monitoring of gas, but not the prediction of gas.There were some limitation of the traditional prediction method, such as modeling subjectivism and statistical prediction. Because it can dynamically adjust the parameters of the model, adaptive prediction method can get the current time according to the prediction error of data and the current time, real-time fault prediction model parameters, this is a very consistent with the prediction method for practical use.This paper presents the gas emission chaos time series method by using volterra series prediction, and on the basis to establish time-series prediction models. The results show that the method not only avoids the phase space reconstruction, but also avoid the points in the neighborhood search, in real-time, with very high efficiency.

38

Andrianajaina, Todizara, David Tsivalalaina Razafimahefa, Raonirivo Rakotoarijaina e Cristian Goyozo Haba. "Grid Search for SARIMAX Parameters for Photovoltaic Time Series Modeling". Global Journal of Energy Technology Research Updates 9 (23 dicembre 2022): 87–96. http://dx.doi.org/10.15377/2409-5818.2022.09.7.

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Abstract (sommario):

The SARIMAX (Seasonal Autoregressive Integrated Moving Average with eXogenous regressors) model is a time series model that can be used to forecast future values of a time series, given its past values. It is beneficial for modeling time series data that exhibits seasonality and incorporating additional exogenous variables (variables that are not part of the time series itself but may affect it). One way to optimize the performance of a SARIMAX model is to use a grid search approach to find the best combination of hyperparameters for the model. A grid search involves specifying a set of possible values for each hyperparameter and then training and evaluating the model using all possible combinations of these values. The combination of hyperparameters that results in the best model performance can then be chosen as the final model. To perform a grid search for a SARIMAX model, you must define the grid of hyperparameters you want to search over. This will typically include the values of the autoregressive (AR) and moving average (MA) terms and the values of any exogenous variables you want to include in the model. We will also need to define a metric to evaluate the model's performance, such as mean absolute or root mean squared error. Once we have defined the grid of hyperparameters and the evaluation metric, you can use a grid search algorithm (such as a brute force search or a more efficient method such as random search or Bayesian optimization) to evaluate the performance of the model using all possible combinations of hyperparameters. The combination of hyperparameters that results in the best model performance can then be chosen as the final model. In this article, we will explore the potential of SARIMAX for PV time series modeling. The objective is to find the optimal set of hyperparameters. Grid Search passes all hyperparameter combinations through the model individually and checks the results. Overall, it returns the collection of hyperparameters that yield the most outstanding results after running the model. One of the most optimal SARIMAX (p,d,q) x (P, D, Q,s) combinations is SARIMAX (0,0,1) x (0,0,0,4).

39

Kashino, Kunio, Gavin A. Smith e Hiroshi Murase. "A quick search algorithm for acoustic signals using histogram features?time-series active search". Electronics and Communications in Japan (Part III: Fundamental Electronic Science) 84, n. 12 (2001): 40–47. http://dx.doi.org/10.1002/ecjc.1055.

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40

Zhan, Peng, Changchang Sun, Yupeng Hu, Wei Luo, Jiecai Zheng e Xueqing Li. "Feature-Based Online Representation Algorithm for Streaming Time Series Similarity Search". International Journal of Pattern Recognition and Artificial Intelligence 34, n. 05 (5 settembre 2019): 2050010. http://dx.doi.org/10.1142/s021800142050010x.

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Abstract (sommario):

With the rapid development of information technology, we have already access to the era of big data. Time series is a sequence of data points associated with numerical values and successive timestamps. Time series not only has the traditional big data features, but also can be continuously generated in a high speed. Therefore, it is very time- and resource-consuming to directly apply the traditional time series similarity search methods on the raw time series data. In this paper, we propose a novel online segmenting algorithm for streaming time series, which has a relatively high performance on feature representation and similarity search. Extensive experimental results on different typical time series datasets have demonstrated the superiority of our method.

41

Jie, Renlong, e Junbin Gao. "Differentiable Neural Architecture Search for High-Dimensional Time Series Forecasting". IEEE Access 9 (2021): 20922–32. http://dx.doi.org/10.1109/access.2021.3055555.

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42

Liu, Bo-ning, Jian-ye Zhang, Peng Zhang e Zhan-lei Wang. "Similarity Search Method in Time Series Based on Curvature Distance". Journal of Electronics & Information Technology 34, n. 9 (9 luglio 2013): 2200–2207. http://dx.doi.org/10.3724/sp.j.1146.2012.00019.

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43

LIN, Zi-Yu, Dong-Qing YANG e Teng-Jiao WANG. "Similarity Search of Time Series with Moving Average Based Indexing". Journal of Software 19, n. 9 (20 settembre 2008): 2349–61. http://dx.doi.org/10.3724/sp.j.1001.2008.02349.

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44

Ofir, Aviv. "Optimizing the search for transiting planets in long time series". Astronomy & Astrophysics 561 (gennaio 2014): A138. http://dx.doi.org/10.1051/0004-6361/201220860.

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45

Thomas, Karen A. "Time-Series Analysis-Spectral Analysis and the Search for Cycles". Western Journal of Nursing Research 12, n. 4 (agosto 1990): 558–62. http://dx.doi.org/10.1177/019394599001200411.

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46

Assent, Ira, Marc Wichterich, Ralph Krieger, Hardy Kremer e Thomas Seidl. "Anticipatory DTW for efficient similarity search in time series databases". Proceedings of the VLDB Endowment 2, n. 1 (agosto 2009): 826–37. http://dx.doi.org/10.14778/1687627.1687721.

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Dowse, Harold B., e John M. Ringo. "The search for hidden periodicities in biological time series revisited". Journal of Theoretical Biology 139, n. 4 (agosto 1989): 487–515. http://dx.doi.org/10.1016/s0022-5193(89)80067-0.

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48

Kedem, Benjamin. "Search for periodicities by axis-crossings of filtered time series". Signal Processing 10, n. 2 (marzo 1986): 129–44. http://dx.doi.org/10.1016/0165-1684(86)90015-0.

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49

Yang, Kiyoung, e Cyrus Shahabi. "An efficient k nearest neighbor search for multivariate time series". Information and Computation 205, n. 1 (gennaio 2007): 65–98. http://dx.doi.org/10.1016/j.ic.2006.08.004.

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Zhang, Ying, Bernard J. Jansen e Amanda Spink. "Time series analysis of a Web search engine transaction log". Information Processing & Management 45, n. 2 (marzo 2009): 230–45. http://dx.doi.org/10.1016/j.ipm.2008.07.003.

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