Relevant bibliographies by topics / Noisy Time Series Clustering

Academic literature on the topic 'Noisy Time Series Clustering'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Noisy Time Series Clustering.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Noisy Time Series Clustering"

1

Tkachenko, Anastasiia Yevhenivna, Liudmyla Olehivna Kyrychenko, and Tamara Anatoliivna Radyvylova. "Clustering Noisy Time Series." System technologies 3, no. 122 (October 10, 2019): 133–39. http://dx.doi.org/10.34185/1562-9945-3-122-2019-15.

Full text
Abstract:
One of the urgent tasks of machine learning is the problem of clustering objects. Clustering time series is used as an independent research technique, as well as part of more complex data mining methods, such as rule detection, classification, anomaly detection, etc.A comparative analysis of clustering noisy time series is carried out. The clustering sample contained time series of various types, among which there were atypical objects. Clustering was performed by k-means and DBSCAN methods using various distance functions for time series.A numerical experiment was conducted to investigate the application of the k-means and DBSCAN methods to model time series with additive white noise. The sample on which clustering was carried out consisted of m time series of various types: harmonic realizations, parabolic realizations, and “bursts”.The work was carried out clustering noisy time series of various types.DBSCAN and k-means methods with different distance functions were used. The best results were shown by the DBSCAN method with the Euclidean metric and the CID function.Analysis of the results of the clustering of time series allows determining the key differences between the methods: if you can determine the number of clusters and you do not need to separate atypical time series, the k-means method shows fairly good results; if there is no information on the number of clusters and there is a problem of isolating non-typical rows, it is advisable to use the DBSCAN method.
APA, Harvard, Vancouver, ISO, and other styles
2

Yelibi, Lionel, and Tim Gebbie. "Agglomerative likelihood clustering." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 11 (November 1, 2021): 113408. http://dx.doi.org/10.1088/1742-5468/ac3661.

Full text
Abstract:
Abstract We consider the problem of fast time-series data clustering. Building on previous work modeling, the correlation-based Hamiltonian of spin variables we present an updated fast non-expensive agglomerative likelihood clustering algorithm (ALC). The method replaces the optimized genetic algorithm based approach (f-SPC) with an agglomerative recursive merging framework inspired by previous work in econophysics and community detection. The method is tested on noisy synthetic correlated time-series datasets with a built-in cluster structure to demonstrate that the algorithm produces meaningful non-trivial results. We apply it to time-series datasets as large as 20 000 assets and we argue that ALC can reduce computation time costs and resource usage costs for large scale clustering for time-series applications while being serialized, and hence has no obvious parallelization requirement. The algorithm can be an effective choice for state-detection for online learning in a fast non-linear data environment, because the algorithm requires no prior information about the number of clusters.
APA, Harvard, Vancouver, ISO, and other styles
3

Zhang, Zheng, Ping Tang, Lianzhi Huo, and Zengguang Zhou. "MODIS NDVI time series clustering under dynamic time warping." International Journal of Wavelets, Multiresolution and Information Processing 12, no. 05 (September 2014): 1461011. http://dx.doi.org/10.1142/s0219691314610116.

Full text
Abstract:
For MODIS NDVI time series with cloud noise and time distortion, we propose an effective time series clustering framework including similarity measure, prototype calculation, clustering algorithm and cloud noise handling. The core of this framework is dynamic time warping (DTW) distance and its corresponding averaging method, DTW barycenter averaging (DBA). We used 12 years of MODIS NDVI time series to perform annual land-cover clustering in Poyang Lake Wetlands. The experimental result shows that our method performs better than classic clustering based on ordinary Euclidean methods.
APA, Harvard, Vancouver, ISO, and other styles
4

Zhang, Yunsheng, Qingzhang Shi, Jiawei Zhu, Jian Peng, and Haifeng Li. "Time Series Clustering with Topological and Geometric Mixed Distance." Mathematics 9, no. 9 (May 6, 2021): 1046. http://dx.doi.org/10.3390/math9091046.

Full text
Abstract:
Time series clustering is an essential ingredient of unsupervised learning techniques. It provides an understanding of the intrinsic properties of data upon exploiting similarity measures. Traditional similarity-based methods usually consider local geometric properties of raw time series or the global topological properties of time series in the phase space. In order to overcome their limitations, we put forward a time series clustering framework, referred to as time series clustering with Topological-Geometric Mixed Distance (TGMD), which jointly considers local geometric features and global topological characteristics of time series data. More specifically, persistent homology is employed to extract topological features of time series and to compute topological similarities among persistence diagrams. The geometric properties of raw time series are captured by using shape-based similarity measures such as Euclidean distance and dynamic time warping. The effectiveness of the proposed TGMD method is assessed by extensive experiments on synthetic noisy biological and real time series data. The results reveal that the proposed mixed distance-based similarity measure can lead to promising results and that it performs better than standard time series analysis techniques that consider only topological or geometrical similarity.
APA, Harvard, Vancouver, ISO, and other styles
5

Zhang, Zheng, Ping Tang, Weixiong Zhang, and Liang Tang. "Satellite Image Time Series Clustering via Time Adaptive Optimal Transport." Remote Sensing 13, no. 19 (October 6, 2021): 3993. http://dx.doi.org/10.3390/rs13193993.

Full text
Abstract:
Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.
APA, Harvard, Vancouver, ISO, and other styles
6

Jacob, Rinku, K. P. Harikrishnan, R. Misra, and G. Ambika. "Weighted recurrence networks for the analysis of time-series data." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475, no. 2221 (January 2019): 20180256. http://dx.doi.org/10.1098/rspa.2018.0256.

Full text
Abstract:
Recurrence networks (RNs) have become very popular tools for the nonlinear analysis of time-series data. They are unweighted and undirected complex networks constructed with specific criteria from time series. In this work, we propose a method to construct a ‘weighted recurrence network’ from a time series and show that it can reveal useful information regarding the structure of a chaotic attractor which the usual unweighted RN cannot provide. Especially, a network measure, the node strength distribution, from every chaotic attractor follows a power law (with exponential cut off at the tail) with an index characteristic to the fractal structure of the attractor. This provides a new class among complex networks to which networks from all standard chaotic attractors are found to belong. Two other prominent network measures, clustering coefficient and characteristic path length, are generalized and their utility in discriminating chaotic dynamics from noise is highlighted. As an application of the proposed measure, we present an analysis of variable star light curves whose behaviour has been reported to be strange non-chaotic in a recent study. Our numerical results indicate that the weighted recurrence network and the associated measures can become potentially important tools for the analysis of short and noisy time series from the real world.
APA, Harvard, Vancouver, ISO, and other styles
7

D’Urso, Pierpaolo, Livia De Giovanni, Riccardo Massari, and Dario Di Lallo. "Noise fuzzy clustering of time series by autoregressive metric." METRON 71, no. 3 (November 2013): 217–43. http://dx.doi.org/10.1007/s40300-013-0024-x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Huang, Mengxing, Qili Bao, Yu Zhang, and Wenlong Feng. "A Hybrid Algorithm for Forecasting Financial Time Series Data Based on DBSCAN and SVR." Information 10, no. 3 (March 7, 2019): 103. http://dx.doi.org/10.3390/info10030103.

Full text
Abstract:
Financial prediction is an important research field in financial data time series mining. There has always been a problem of clustering massive financial time series data. Conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several financial forecasting models. In this paper, a new hybrid algorithm is proposed based on Optimization of Initial Points and Variable-Parameter Density-Based Spatial Clustering of Applications with Noise (OVDBCSAN) and support vector regression (SVR). At the initial point of optimization, ε and MinPts, which are global parameters in DBSCAN, mainly deal with datasets of different densities. According to different densities, appropriate parameters are selected for clustering through optimization. This algorithm can find a large number of similar classes and then establish regression prediction models. It was tested extensively using real-world time series datasets from Ping An Bank, the Shanghai Stock Exchange, and the Shenzhen Stock Exchange to evaluate accuracy. The evaluation showed that our approach has major potential in clustering massive financial time series data, therefore improving the accuracy of the prediction of stock prices and financial indexes.
APA, Harvard, Vancouver, ISO, and other styles
9

Li, Haibo, Cheng Wang, Gengqian Wei, and Sina Xu. "Mining the Coopetition Relationship of Urban Public Traffic Lines Based on Time Series Correlation." Journal of Physics: Conference Series 2138, no. 1 (December 1, 2021): 012005. http://dx.doi.org/10.1088/1742-6596/2138/1/012005.

Full text
Abstract:
Abstract Along with the evolution of passenger flows within cities, the coordination between public traffic lines should be sustainably optimized with respect to the spatial distribution of the flow, though the lines were planned well at the beginning of the construction. It is critical to determine the coopetition between bus lines to optimize a transit network continuously. A method of mining coopetition relationship (MCBTC, Mining Coopetition relationship between Bus lines based on a Time series Correlation) based on passenger flow is proposed in this study. First, noisy, inconsistent or missing data are eliminated to obtain a passenger flow time series, and the proposed merging algorithm is used to extract the line passenger flow time series (LPFTS, Line Passenger Flow Time Series) by merging the passenger flow of adjacent buses from the same line. Then, to calculate the positive and negative correlation sequence sets, a clustering algorithm is proposed. The two sequence sets represent the competition and cooperation relationships, respectively. The MCBTC method has been tested with a practical data set, and the results show that it is very promising.
APA, Harvard, Vancouver, ISO, and other styles
10

Kuschnerus, Mieke, Roderik Lindenbergh, and Sander Vos. "Coastal change patterns from time series clustering of permanent laser scan data." Earth Surface Dynamics 9, no. 1 (February 19, 2021): 89–103. http://dx.doi.org/10.5194/esurf-9-89-2021.

Full text
Abstract:
Abstract. Sandy coasts are constantly changing environments governed by complex, interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and to support analysis of geomorphological deformation processes. This novel technique delivers 3-D representations of the coast at hourly temporal and centimetre spatial resolution and allows us to observe small-scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatiotemporal data set is needed. To enable automated data mining, we extract time series of surface elevation and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well-known clustering algorithms (k-means clustering, agglomerative clustering and density-based spatial clustering of applications with noise; DBSCAN), apply them on the set of time series and identify areas that undergo similar evolution during 1 month. We test if these algorithms fulfil our criteria for suitable clustering on our exemplary data set. The three clustering methods are applied to time series over 30 d extracted from a data set of daily scans covering about 2 km of coast in Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer, is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow us to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The level of detail found with these algorithms depends on the choice of the number of clusters k. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused, for example, by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatiotemporal data set for predominant deformation patterns with the associated regions where they occur.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Noisy Time Series Clustering"

1

Kim, Doo Young. "Statistical Modeling of Carbon Dioxide and Cluster Analysis of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, and Multi-Level Time Series Clustering." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6277.

Full text
Abstract:
The current study consists of three major parts. Statistical modeling, the connection between statistical modeling and cluster analysis, and proposing new methods to cluster time dependent information. First, we perform a statistical modeling of the Carbon Dioxide (CO2) emission in South Korea in order to identify the attributable variables including interaction effects. One of the hot issues in the earth in 21st century is Global warming which is caused by the marriage between atmospheric temperature and CO2 in the atmosphere. When we confront this global problem, we first need to verify what causes the problem then we can find out how to solve the problem. Thereby, we find and rank the attributable variables and their interactions based on their semipartial correlation and compare our findings with the results from the United States and European Union. This comparison shows that the number one contributing variable in South Korea and the United States is Liquid Fuels while it is the number 8 ranked in EU. This comparison provides the evidence to support regional policies and not global, to control CO2 in an optimal level in our atmosphere. Second, we study regional behavior of the atmospheric CO2 in the United States. Utilizing the longitudinal transitional modeling scheme, we calculate transitional probabilities based on effects from five end-use sectors that produce most of the CO2 in our atmosphere, that is, the commercial sector, electric power sector, industrial sector, residential sector, and the transportation sector. Then, using those transitional probabilities we perform a hierarchical clustering procedure to classify the regions with similar characteristics based on nine US climate regions. This study suggests that our elected officials can proceed to legislate regional policies by end-use sectors in order to maintain the optimal level of the atmospheric CO2 which is required by global consensus. Third, we propose new methods to cluster time dependent information. It is almost impossible to find data that are not time dependent among floods of information that we have nowadays, and it needs not to emphasize the importance of data mining of the time dependent information. The first method we propose is called “Lag Target Time Series Clustering (LTTC)” which identifies actual level of time dependencies among clustering objects. The second method we propose is the “Multi-Factor Time Series Clustering (MFTC)” which allows us to consider the distance in multi-dimensional space by including multiple information at a time. The last method we propose is the “Multi-Level Time Series Clustering (MLTC)” which is especially important when you have short term varying time series responses to cluster. That is, we extract only pure lag effect from LTTC. The new methods that we propose give excellent results when applied to time dependent clustering. Finally, we develop appropriate algorithm driven by the analytical structure of the proposed methods to cluster financial information of the ten business sectors of the N.Y. Stock Exchange. We used in our clustering scheme 497 stocks that constitute the S&P 500 stocks. We illustrated the usefulness of the subject study by structuring diversified financial portfolio.
APA, Harvard, Vancouver, ISO, and other styles
2

Xiong, Yimin. "Time series clustering using ARMA models /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20XIONG.

Full text
Abstract:
Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.
Includes bibliographical references (leaves 49-55). Also available in electronic version. Access restricted to campus users.
APA, Harvard, Vancouver, ISO, and other styles
3

Jarjour, Riad. "Clustering financial time series for volatility modeling." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6439.

Full text
Abstract:
The dynamic conditional correlation (DCC) model and its variants have been widely used in modeling the volatility of multivariate time series, with applications in portfolio construction and risk management. While popular for its simplicity, the DCC uses only two parameters to model the correlation dynamics, regardless of the number of assets. The flexible dynamic conditional correlation (FDCC) model attempts to remedy this by grouping the stocks into various clusters, each with its own set of parameters. However, it assumes the grouping is known apriori. In this thesis we develop a systematic method to determine the number of groups to use as well as how to allocate the assets to groups. We show through simulation that the method does well in identifying the groups, and apply the method to real data, showing its performance. We also develop and apply a Bayesian approach to this same problem. Furthermore, we propose an instantaneous measure of correlation that can be used in many volatility models, and in fact show that it outperforms the popular sample Pearson's correlation coefficient for small sample sizes, thus opening the door to applications in fields other than finance.
APA, Harvard, Vancouver, ISO, and other styles
4

Torku, Thomas K. "Takens Theorem with Singular Spectrum Analysis Applied to Noisy Time Series." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etd/3013.

Full text
Abstract:
The evolution of big data has led to financial time series becoming increasingly complex, noisy, non-stationary and nonlinear. Takens theorem can be used to analyze and forecast nonlinear time series, but even small amounts of noise can hopelessly corrupt a Takens approach. In contrast, Singular Spectrum Analysis is an excellent tool for both forecasting and noise reduction. Fortunately, it is possible to combine the Takens approach with Singular Spectrum analysis (SSA), and in fact, estimation of key parameters in Takens theorem is performed with Singular Spectrum Analysis. In this thesis, we combine the denoising abilities of SSA with the Takens theorem approach to make the manifold reconstruction outcomes of Takens theorem less sensitive to noise. In particular, in the course of performing the SSA on a noisy time series, we branch of into a Takens theorem approach. We apply this approach to a variety of noisy time series.
APA, Harvard, Vancouver, ISO, and other styles
5

Li, Jing. "Clustering and forecasting for rain attenuation time series data." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219615.

Full text
Abstract:
Clustering is one of unsupervised learning algorithm to group similar objects into the same cluster and the objects in the same cluster are more similar to each other than those in the other clusters. Forecasting is making prediction based on the past data and efficient artificial intelligence models to predict data developing tendency, which can help to make appropriate decisions ahead. The datasets used in this thesis are the signal attenuation time series data from the microwave networks. Microwave networks are communication systems to transmit information between two fixed locations on the earth. They can support increasing capacity demands of mobile networks and play an important role in next generation wireless communication technology. But inherent vulnerability to random fluctuation such as rainfall will cause significant network performance degradation. In this thesis, K-means, Fuzzy c-means and 2-state Hidden Markov Model are used to develop one step and two step rain attenuation data clustering models. The forecasting models are designed based on k-nearest neighbor method and implemented with linear regression to predict the real-time rain attenuation in order to help microwave transport networks mitigate rain impact, make proper decisions ahead of time and improve the general performance.
Clustering is een van de unsupervised learning algorithmen om groep soortgelijke objecten in dezelfde cluster en de objecten in dezelfde cluster zijn meer vergelijkbaar met elkaar dan die in de andere clusters. Prognoser är att göra förutspårningar baserade på övergående data och effektiva artificiella intelligensmodeller för att förutspå datautveckling, som kan hjälpa till att fatta lämpliga beslut. Dataseten som används i denna avhandling är signaldämpningstidsseriedata från mikrovågsnätverket. Mikrovågsnät är kommunikationssystem för att överföra information mellan två fasta platser på jorden. De kan stödja ökade kapacitetsbehov i mobilnät och spela en viktig roll i nästa generationens trådlösa kommunikationsteknik. Men inneboende sårbarhet för slumpmässig fluktuering som nedbörd kommer att orsaka betydande nätverksförstöring. I den här avhandlingen används K-medel, Fuzzy c-medel och 2-state Hidden Markov Model för att utveckla ett steg och tvåstegs regen dämpning dataklyvningsmodeller. Prognosmodellerna är utformade utifrån k-närmaste granne-metoden och implementeras med linjär regression för att förutsäga realtidsdämpning för att hjälpa mikrovågstransportnät att mildra regnpåverkan, göra rätt beslut före tid och förbättra den allmänna prestandan.
APA, Harvard, Vancouver, ISO, and other styles
6

Nunes, Neuza Filipa Martins. "Algorithms for time series clustering applied to biomedical signals." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/5666.

Full text
Abstract:
Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering
The increasing number of biomedical systems and applications for human body understanding creates a need for information extraction tools to use in biosignals. It’s important to comprehend the changes in the biosignal’s morphology over time, as they often contain critical information on the condition of the subject or the status of the experiment. The creation of tools that automatically analyze and extract relevant attributes from biosignals, providing important information to the user, has a significant value in the biosignal’s processing field. The present dissertation introduces new algorithms for time series clustering, where we are able to separate and organize unlabeled data into different groups whose signals are similar to each other. Signal processing algorithms were developed for the detection of a meanwave, which represents the signal’s morphology and behavior. The algorithm designed computes the meanwave by separating and averaging all cycles of a cyclic continuous signal. To increase the quality of information given by the meanwave, a set of wave-alignment techniques was also developed and its relevance was evaluated in a real database. To evaluate our algorithm’s applicability in time series clustering, a distance metric created with the information of the automatic meanwave was designed and its measurements were given as input to a K-Means clustering algorithm. With that purpose, we collected a series of data with two different modes in it. The produced algorithm successfully separates two modes in the collected data with 99.3% of efficiency. The results of this clustering procedure were compared to a mechanism widely used in this area, which models the data and uses the distance between its cepstral coefficients to measure the similarity between the time series.The algorithms were also validated in different study projects. These projects show the variety of contexts in which our algorithms have high applicability and are suitable answers to overcome the problems of exhaustive signal analysis and expert intervention. The algorithms produced are signal-independent, and therefore can be applied to any type of signal providing it is a cyclic signal. The fact that this approach doesn’t require any prior information and the preliminary good performance make these algorithms powerful tools for biosignals analysis and classification.
APA, Harvard, Vancouver, ISO, and other styles
7

Correia, Maria Inês Costa. "Cluster analysis of financial time series." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/21016.

Full text
Abstract:
Mestrado em Mathematical Finance
Esta dissertação aplica o método da Signature como medida de similaridade entre dois objetos de séries temporais usando as propriedades de ordem 2 da Signature e aplicando-as a um método de Clustering Asimétrico. O método é comparado com uma abordagem de Clustering mais tradicional, onde a similaridade é medida usando Dynamic Time Warping, desenvolvido para trabalhar com séries temporais. O intuito é considerar a abordagem tradicional como benchmark e compará-la ao método da Signature através do tempo de computação, desempenho e algumas aplicações. Estes métodos são aplicados num conjunto de dados de séries temporais financeiras de Fundos Mútuos do Luxemburgo. Após a revisão da literatura, apresentamos o método Dynamic Time Warping e o método da Signature. Prossegue-se com a explicação das abordagens de Clustering Tradicional, nomeadamente k-Means, e Clustering Espectral Assimétrico, nomeadamente k-Axes, desenvolvido por Atev (2011). O último capítulo é dedicado à Investigação Prática onde os métodos anteriores são aplicados ao conjunto de dados. Os resultados confirmam que o método da Signature têm efectivamente potencial para Machine Learning e previsão, como sugerido por Levin, Lyons and Ni (2013).
This thesis applies the Signature method as a measurement of similarities between two time-series objects, using the Signature properties of order 2, and its application to Asymmetric Spectral Clustering. The method is compared with a more Traditional Clustering approach where similarities are measured using Dynamic Time Warping, developed to work with time-series data. The intention for this is to consider the traditional approach as a benchmark and compare it to the Signature method through computation times, performance, and applications. These methods are applied to a financial time series data set of Mutual Exchange Funds from Luxembourg. After the literature review, we introduce the Dynamic Time Warping method and the Signature method. We continue with the explanation of Traditional Clustering approaches, namely k-Means, and Asymmetric Clustering techniques, namely the k-Axes algorithm, developed by Atev (2011). The last chapter is dedicated to Practical Research where the previous methods are applied to the data set. Results confirm that the Signature method has indeed potential for machine learning and prediction, as suggested by Levin, Lyons, and Ni (2013).
info:eu-repo/semantics/publishedVersion
APA, Harvard, Vancouver, ISO, and other styles
8

Nelson, Alex Tremain. "Nonlinear estimation and modeling of noisy time-series by dual Kalman filtering methods." Oregon Health & Science University, 2000. http://content.ohsu.edu/u?/etd,211.

Full text
Abstract:
Ph.D.
Electrical and Computer Engineering
Numerous applications require either the estimation or prediction of a noisy time-series. Examples include speech enhancement, economic forecasting, and geophysical modeling. A noisy time-series can be described in terms of a probabilistic model, which accounts for both the deterministic and stochastic components of the dynamics. Such a model can be used with a Kalman filter (or extended Kalman filter) to estimate and predict the time-series from noisy measurements. When the model is unknown, it must be estimated as well; dual estimation refers to the problem of estimating both the time-series, and its underlying probabilistic model, from noisy data. The majority of dual estimation techniques in the literature are for signals described by linear models, and many are restricted to off-line application domains. Using a probabilistic approach to dual estimation, this work unifies many of the approaches in the literature within a common theoretical and algorithmic framework, and extends their capabilities to include sequential dual estimation of both linear and nonlinear signals. The dual Kalman filtering method is developed as a method for minimizing a variety of dual estimation cost functions, and is shown to be an effective general method for estimating the signal, model parameters, and noise variances in both on-line and off-line environments.
APA, Harvard, Vancouver, ISO, and other styles
9

Wang, Chiying. "Contributions to Collective Dynamical Clustering-Modeling of Discrete Time Series." Digital WPI, 2016. https://digitalcommons.wpi.edu/etd-dissertations/198.

Full text
Abstract:
The analysis of sequential data is important in business, science, and engineering, for tasks such as signal processing, user behavior mining, and commercial transactions analysis. In this dissertation, we build upon the Collective Dynamical Modeling and Clustering (CDMC) framework for discrete time series modeling, by making contributions to clustering initialization, dynamical modeling, and scaling. We first propose a modified Dynamic Time Warping (DTW) approach for clustering initialization within CDMC. The proposed approach provides DTW metrics that penalize deviations of the warping path from the path of constant slope. This reduces over-warping, while retaining the efficiency advantages of global constraint approaches, and without relying on domain dependent constraints. Second, we investigate the use of semi-Markov chains as dynamical models of temporal sequences in which state changes occur infrequently. Semi-Markov chains allow explicitly specifying the distribution of state visit durations. This makes them superior to traditional Markov chains, which implicitly assume an exponential state duration distribution. Third, we consider convergence properties of the CDMC framework. We establish convergence by viewing CDMC from an Expectation Maximization (EM) perspective. We investigate the effect on the time to convergence of our efficient DTW-based initialization technique and selected dynamical models. We also explore the convergence implications of various stopping criteria. Fourth, we consider scaling up CDMC to process big data, using Storm, an open source distributed real-time computation system that supports batch and distributed data processing. We performed experimental evaluation on human sleep data and on user web navigation data. Our results demonstrate the superiority of the strategies introduced in this dissertation over state-of-the-art techniques in terms of modeling quality and efficiency.
APA, Harvard, Vancouver, ISO, and other styles
10

Nordlinder, Magnus. "Clustering of Financial Account Time Series Using Self Organizing Maps." Thesis, KTH, Matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291612.

Full text
Abstract:
This thesis aims to cluster financial account time series by extracting global features from the time series and by using two different dimensionality reduction methods, Kohonen Self Organizing Maps and principal component analysis, to cluster the set of the time series by using K-means. The results are then used to further cluster a set of financial services provided by a financial institution, to determine if it is possible to find a set of services which coincide with the time series clusters. The results find several sets of services that are prevalent in the different time series clusters. The resulting method can be used to understand the dynamics between deposits variability and the customers usage of different services and to analyse whether a service is more used in different clusters.
Målet med denna uppsats är att klustra tidsserier över finansiella konton genom att extrahera tidsseriernas karakteristik. För detta används två metoder för att reducera tidsseriernas dimensionalitet, Kohonen Self Organizing Maps och principal komponent analys. Resultatet används sedan för att klustra finansiella tjänster som en kund använder, med syfte att analysera om det existerar ett urval av tjänster som är mer eller mindre förekommande bland olika tidsseriekluster. Resultatet kan användas för att analysera dynamiken mellan kontobehållning och kundens finansiella tjänster, samt om en tjänst är mer förekommande i ett tidsseriekluster.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Noisy Time Series Clustering"

1

Time Series Clustering and Classification. Chapman and Hall/CRC, 2019.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Whitenack, Daniel. Machine Learning With Go: Implement Regression, Classification, Clustering, Time-series Models, Neural Networks, and More using the Go Programming Language. Packt Publishing - ebooks Account, 2017.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Data Preprocessing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0006.

Full text
Abstract:
Successful reconstruction of a shadow attractor provides preliminary empirical evidence that a signal isolated from observed time series data may be generated by deterministic dynamics. However, because we cannot reasonably expect signal processing to purge the signal of all noise in practice, and because noisy linear behavior can be visually indistinguishable from nonlinear behavior, the possibility remains that noticeable regularity detected in a shadow attractor may be fortuitously reconstructed from data generated by a linear-stochastic process. This chapter investigates how we can test this null hypothesis using surrogate data testing. The combination of a noticeably regular shadow attractor, along with strong statistical rejection of fortuitous regularity, increases the probability that observed data are generated by deterministic real-world dynamics.
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Noisy Time Series Clustering"

1

Alanzado, Arnold C., and Sadaaki Miyamoto. "Fuzzy c-Means Clustering in the Presence of Noise Cluster for Time Series Analysis." In Modeling Decisions for Artificial Intelligence, 156–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11526018_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Basalto, Nicolas, and Francesco De Carlo. "Clustering financial time series." In Practical Fruits of Econophysics, 252–56. Tokyo: Springer Tokyo, 2006. http://dx.doi.org/10.1007/4-431-28915-1_46.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Gupta, Kartikay, and Niladri Chatterjee. "Financial Time Series Clustering." In Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2, 146–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63645-0_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Leamer, Edward E. "Pooling Noisy Data Sets." In Econometrics of Short and Unreliable Time Series, 41–60. Heidelberg: Physica-Verlag HD, 1995. http://dx.doi.org/10.1007/978-3-642-99782-2_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Cheng, B., and H. Tong. "Nonparametric function estimation in noisy chaos." In Developments in Time Series Analysis, 183–206. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4515-0_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Good, Phillip. "Clustering in Time and Space." In Springer Series in Statistics, 105–9. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-2346-5_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Good, Phillip. "Clustering in Time and Space." In Springer Series in Statistics, 134–39. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4757-3235-1_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Pinto da Costa, Joaquim. "Weighted Clustering of Time Series." In Rankings and Preferences, 69–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48344-2_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Wang, Fei, and Changshui Zhang. "Spectral Clustering for Time Series." In Pattern Recognition and Data Mining, 345–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_37.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Marti, Gautier, Frank Nielsen, Philippe Very, and Philippe Donnat. "Clustering Random Walk Time Series." In Lecture Notes in Computer Science, 675–84. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25040-3_72.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Noisy Time Series Clustering"

1

Rivera-García, Diego, Luis Angel García-Escudero, Agustín Mayo-Iscar, and Joaquin Ortega. "Stationary Intervals for Random Waves by Functional Clustering of Spectral Densities." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-19171.

Full text
Abstract:
Abstract A new time series clustering procedure, based on Functional Data Analysis techniques applied to spectral densities, is employed in this work for the detection of stationary intervals in random waves. Long records of wave data are divided into 30-minute or one-hour segments and the spectral density of each interval is estimated by one of the standard methods available. These spectra are regarded as the main characteristic of each 30-minute time series for clustering purposes. The spectra are considered as functional data and, after representation on a spline basis, they are clustered by a mixtures model method based on a truncated Karhunen-Loéve expansion as an approximation to the density function for functional data. The clustering method uses trimming techniques and restrictions on the scatter within groups to reduce the effect of outliers and to prevent the detection of spurious clusters. Simulation examples show that the procedure works well in the presence of noise and the restrictions on the scatter are effective in avoiding the detection of false clusters. Consecutive time intervals clustered together are considered as a single stationary segment of the time series. An application to real wave data is presented.
APA, Harvard, Vancouver, ISO, and other styles
2

Upadhyay, Priyadarshi, S. K. Ghosh, and Anil Kumar. "Entropy based noise clustering soft classification method for identification of wheat crop using time series MODIS data." In 2014 Third International Conference on Agro-Geoinformatics. IEEE, 2014. http://dx.doi.org/10.1109/agro-geoinformatics.2014.6910670.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Khalil, Benmouiza, and Cheknane Ali. "Density-based spatial clustering of application with noise algorithm for the classification of solar radiation time series." In 2016 8th International Conference on Modelling, Identification and Control (ICMIC). IEEE, 2016. http://dx.doi.org/10.1109/icmic.2016.7804123.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Cawley, Robert, Guan-Hsong Hsu, and Liming W. Salvino. "Detecting smoothness in noisy time series." In Chaotic, fractal, and nonlinear signal processing. AIP, 1996. http://dx.doi.org/10.1063/1.51053.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Khadivi, Pejman, Prithwish Chakraborty, Ravi Tandon, and Naren Ramakrishnan. "Time series forecasting via noisy channel reversal." In 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2015. http://dx.doi.org/10.1109/mlsp.2015.7324330.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Bhat, Harish S., Majerle Reeves, and Ramin Raziperchikolaei. "Estimating Vector Fields from Noisy Time Series." In 2020 54th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2020. http://dx.doi.org/10.1109/ieeeconf51394.2020.9443354.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Yue, Jianwei, Brian Franczak, Glen Takahara, and Wesley S. Burr. "Time Series Clustering using Coherence." In 2nd International Conference on Statistics: Theory and Applications (ICSTA'20). Avestia Publishing, 2020. http://dx.doi.org/10.11159/icsta20.136.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Chis, Monica, and Crina Grosan. "Evolutionary Hierarchical Time Series Clustering." In Sixth International Conference on Intelligent Systems Design and Applications]. IEEE, 2006. http://dx.doi.org/10.1109/isda.2006.144.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Ping, Loh Wei, Yahya Abu Hasan, Kamel Ariffin Mohd Atan, and Isthrinayagy S. Krishnarajah. "Clustering Short Time-Series Microarray." In INTERNATIONAL CONFERENCE ON MATHEMATICAL BIOLOGY 2007: ICMB07. AIP, 2008. http://dx.doi.org/10.1063/1.2883864.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Dougherty, Edward R., Junior Barrera, Marcel Brun, Seungchan Kim, Roberto M. Cesar, Yidong Chen, Michael L. Bittner, and Jeffrey M. Trent. "Time series inference from clustering." In BiOS 2001 The International Symposium on Biomedical Optics, edited by Michael L. Bittner, Yidong Chen, Andreas N. Dorsel, and Edward R. Dougherty. SPIE, 2001. http://dx.doi.org/10.1117/12.427991.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Noisy Time Series Clustering"

1

Perr-Sauer, Jordan, Adam W. Duran, and Caleb T. Phillips. Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1597242.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Blakely, Logan. Spectral Clustering for Electrical Phase Identification Using Advanced Metering Infrastructure Voltage Time Series. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6567.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Noisy Time Series Clustering' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography