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Identification of Concurrent Control Chart Patterns in Time Series

Shi lpy Sharma, David A Swayne Charlie Obimbo
2015 International Journal of Innovative Research in Science, Engineering and Technology  
It will identify independent components hidden in mixture patterns and input those independent components to decision trees for recognition of as many as eight separate control chart patterns.  ...  From our experimental results, it can be concluded that proposed scheme may efficiently analyzemixture patterns in time-series of medical, financial and any other applications.  ...  An ICA has been successfully applied in various fields of multivariate data procession from image processing, face recognition to time series prediction [14] .  ... 
doi:10.15680/ijirset.2015.0406167 fatcat:6vjnft46x5h3lg3h2wj3hio7pq

Identifying Temporal Patterns for Characterization and Prediction of Financial Time Series Events [chapter]

Richard J. Povinelli
2001 Lecture Notes in Computer Science  
The TSDM framework and concepts are reviewed, and the applicable TSDM method is discussed. Finally, the TSDM method is applied to time series generated by a basket of financial securities.  ...  The novel Time Series Data Mining (TSDM) framework is applied to analyzing financial time series. The TSDM framework adapts and innovates data mining concepts to analyzing time series data.  ...  Review of Time Series Analysis Techniques The analysis of financial time series has a long history.  ... 
doi:10.1007/3-540-45244-3_5 fatcat:fzc3dzlzpje65dmghvrnacjoza

Recent Techniques of Clustering of Time Series Data: A Survey

Sangeeta Rani, Geeta Sikka
2012 International Journal of Computer Applications  
Time-Series clustering is one of the important concepts of data mining that is used to gain insight into the mechanism that generate the time-series and predicting the future values of the given time-series  ...  In this paper, we have shown the survey and summarization of previous work that investigated the clustering of time series in various application domains ranging from science, engineering, business, finance  ...  component analysis Euclidean Modified k-means Real world stock time- series Jian Xin Wu Independent component analysis N/A support vector regression Financial time-series Geert Verdoolaege  ... 
doi:10.5120/8282-1278 fatcat:v5d6vcatmrdnvjj55tubb736m4

Application of improved convolution neural network in financial forecasting

2022 Journal of Organizational and End User Computing  
Firstly, the noise of the collected data set is deleted, and then the clustering result is more stable by principal component analysis.  ...  In order to deduce the source of monetary funds and determine their whereabouts, financial information and prediction have become a scientific method that can not be ignored in the development of national  ...  pattern in time series data, which is related to the nature of financial information and can be understood in time.  ... 
doi:10.4018/joeuc.289222 fatcat:orvbofmj55hv5i33ssg3cymfgq

Real Time Iris Image Segmentation for Non Co-Operative Environment

Patil MayurJ., K. P. Adhiya
2014 International Journal of Computer Applications  
as Independent Component Analysis (ICA).  ...  To find a linear representation of non gaussian data, Independent Component Analysis is used & because of this the components are statistically independent, or as independent as possible.  ... 
doi:10.5120/15273-3944 fatcat:iovtnxlnijcfrbwnk4uod2junu

Pattern Classification of Signals Using Fisher Kernels

Yashodhan Athavale, Sridhar Krishnan, Aziz Guergachi
2012 Mathematical Problems in Engineering  
The intention of this study is to gauge the performance of Fisher kernels for dimension simplification and classification of time-series signals.  ...  For assessing the fate of each company, we have collected financial time-series composed of weekly closing stock prices in a common time frame, using Thomson Datastream software.  ...  Acknowledgments The authors extend their sincere thanks to NSERC Granting Council, CFI/OIT, and Canada Research Chairs program for funding the research.  ... 
doi:10.1155/2012/467175 fatcat:faxkpf3gmfc3xlbomhkqy2prci

Irregularity, volatility, risk, and financial market time series

S. Pincus, R. E. Kalman
2004 Proceedings of the National Academy of Sciences of the United States of America  
instability ͉ random walk S eries of sequential data are pivotal to much of financial analysis.  ...  The purpose of this article is to demonstrate several applications of ApEn to the evaluation of financial data.  ...  To better understand the utility of ApEn as applied herein, we consider the extant approaches to financial time series analysis.  ... 
doi:10.1073/pnas.0405168101 pmid:15358860 pmcid:PMC518821 fatcat:chd2i6miy5fp3pw54z36vd5kvy

Neural Networks and Empirical Research in Accounting

Duarte Trigueiros, Richard Taffler
1996 Accounting and Business Research  
This article seeks to provide an overview of the potential role of neural network (connectionist) meth<>dalogy in empirical accounting research.  ...  A non-technical overview of neural network methodology is givelL along with guidelines to help accounting researchers interested in applying these new tools to recognise the potential dangers and strengths  ...  Drawing on the substantial literature applying connectionist approaches of a pattern recognition nature in financial forecasting, such as real-time market trading and technical analysis (eg., Baestaens  ... 
doi:10.1080/00014788.1996.9729524 fatcat:t3nezgyqabbjzkajxy275c6upe

Pattern recognition using hidden Markov models in financial time series

Sara Rebagliati, Emanuela Sasso
2017 Acta et Commentationes Universitatis Tartuensis de Mathematica  
The classication algorithm correctly recognizes 93% of the provided patterns. Thanks to the analysis of the false positive examples, we nally designed some more lters to reduce them.  ...  Our aim consists in developing a software which can recognize M trading patterns in real time using Hidden Markov Models (HMMs).  ...  The authors wish to thank the referees for the remarks which helped to improve the version of the paper.  ... 
doi:10.12697/acutm.2017.21.02 fatcat:jgsl5zrxl5czln5ulqxaahj3pi

The application of dynamic self-organised multilayer network inspired by the Immune Algorithm for weather signals forecast

Abir Jaafar Hussain, Dhiya Al-Jumeily, Haya Al-Askar
2015 2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)  
, medical diagnostics and pattern recognition for big data.  ...  This can extend the application of the proposed network to scientifically analyse different types of big data.  ...  ANNs have been proposed as useful tools in time series analysis in a variety of applications.  ... 
doi:10.1109/taeece.2015.7113607 fatcat:asar63xgmjhejd2dtj7hn7oc5u

The Use of Artificial Intelligence in Building Automated Trading Systems

Jan Juricek
2014 Journal of clean energy technologies  
It summarizes the findings of systemic approaches over building trade system and an application of the AI (mainly genetic algorithms and neural networks) to find the best solutions, while the use of artificial  ...  The article introduces the reader with the concept of long term success in trading in financial markets.  ...  Leading analysts have tried to disaggregate the market behavior by time series models. Decomposition of time series (see above) is the simplest approach to the modeling of time series [7] , [12] .  ... 
doi:10.7763/ijcte.2014.v6.883 fatcat:dud4glbtdzfubntzz7s5dpnmt4

Speech Recognition in ATMs: Application of Linear Predictive Coding and Support Vector Machines

Dr. Sonia Sunny
2017 International Journal for Research in Applied Science and Engineering Technology  
This hybrid architecture of LPC and SVM produced rather good recognition accuracy of 80.25%.  ...  Today, Automated Teller Machines (ATMs) are extensively used by people for financial transactions. It provides a convenient, fast and easy way for customers to access cash.  ...  CONCLUSIONS The research in the domain of audio and speech recognition is still going on and this work is an application of speech recognition for financial transactions in ATMs.  ... 
doi:10.22214/ijraset.2017.3100 fatcat:h6lkb5f2ofeilikv647rcgs5lu

Gabor filter for enhanced recognition of assisted turning events

N. S. Padhye, Xuan Zhang, M. P. Rapp, N. Bergstrom
2011 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
In the RCT, it is necessary to objectively confirm the reported times of assisted turning by certified nurse assistants using activity data.  ...  Activity peaks in Gaussian-smoothed activity data were unable to confirm all turning events in a supporting pilot study with observer notes of assisted turns.  ...  Acknowledgments This work was supported by the National Institutes of Health under grant R01 NR009680.  ... 
doi:10.1109/iembs.2011.6091941 pmid:22256165 pmcid:PMC3601032 dblp:conf/embc/PadhyeZRB11 fatcat:spwp5yd6yvajlpbd47daosw56i

Mining and Forecasting of Big Time-series Data

Yasushi Sakurai, Yasuko Matsubara, Christos Faloutsos
2015 Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data - SIGMOD '15  
of time-series mining and tensor analysis.  ...  Time-series data analysis is becoming of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-line processing capability.  ...  Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.  ... 
doi:10.1145/2723372.2731081 dblp:conf/sigmod/SakuraiMF15 fatcat:dsmv2sqs35bm5ifeqr4qnuz6ty

Adaptive nonstationary regression analysis

O. Krasotkina, V. Mottl
2008 Pattern Recognition (ICPR), Proceedings of the International Conference on  
from the full stationarity of instant models to their absolute independence in time.  ...  The problem of finding the most appropriate subset of features or regressors is the generic challenge of Machine Learning problems like regression estimation or pattern recognition.  ...  We consider the time series to be processed ( , 1,..., ) t y t N = (2) as the observable part of a two-component random process, whose hidden part is the unknown sequence of time-varying regression coef  ... 
doi:10.1109/icpr.2008.4761666 dblp:conf/icpr/KrasotkinaM08 fatcat:kac3pkzi75dg3da7sqqh76mcge
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