[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122591-en":3,"doc-seo-122591-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122591,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Estimation of Correlation Matrices from Limited Time Series Data using Machine Learning","Prediction of correlation matrices from given time series data supports applications such as inferring neuronal connections from spiking signals, identifying causal gene dependencies from expression measurements, and capturing long-range climatic influences. The work addresses the limitation of traditional approaches that require time series from all network nodes by introducing a supervised machine learning method to predict whole-system correlation matrices from finite observations of only a few randomly selected nodes. Model accuracy is validated, and unsupervised analysis is used for interpretability. The approach is further tested on real-world datasets, including EEG, to confirm practical effectiveness.","arXiv :2209 .0 1 198v 3 [ cs .LG] 7 Oct 2022  \nEstimation of Correlation Matrices from Limited time series Data using  \nMachine Learning  \nNikhil Easawa,, Woo Seok Leeb,c , Prashant Singh Lohiyaa , Sarika Jalana , Priodyuti Pradhand  \na Complex Systems Lab, Department of Physics, Indian Institute of Technology Indore, Khandwa Road, Simrol,  \nIndore-453552, India  \nb Center for Theoretical Physics of Complex Systems, Institute for Basic Science (IBS), Daejeon 34126, Republic of  \nKorea  \nc 1ST Biotherapeutics, Inc., Seongnam, 13493, Republic of Korea  \nd School of Computer Science, University of Petroleum and Energy Studies, Dehradun - 248007, India  \nAbstract  \nPrediction of correlation matrices from given time series data has several applications for a range of problems, such as inferring neuronal connections from spiking data, deducing causal dependencies between genes from expression data, and discovering long spatial range in􀀍uences in climate variations. Traditional methods of predicting correlation matrices utilize time series data of all the nodes of the underlying networks. Here, we use a supervised machine learning technique to predict the correlation matrix of entire systems from 􀀌nite time series information of a few randomly selected nodes. The accuracy of the prediction from the model con􀀌rms that only a limited time series of a subset of the entire system is enough to make good correlation matrix predictions. Furthermore, using an unsupervised learning algorithm, we provide insights into the success of the predictions from our model. Finally, we apply the machine learning model developed here to real-world datasets.  \nKeywords: Time series data, Correlation matrix, Non-linear dynamics, Machine learning, Complex networks  \n1. Introduction  \nMachine learning has been applied in diverse areas of physical sciences ranging from condensed matter to high energy physics to complex systems. In complex systems, neural network-based machine learning techniques have been used in predicting amplitude death [1], anticipation of synchronization [2], phase transitions in complex networks [3], time series prediction [4], etc. In particular, forecasting time series data has attracted interest from the scienti􀀌c fraternity due to its diverse applications in real-world dynamical systems like stock markets and the brain. However, predicting the time series of a dynamical system has many limitations [5, 6] . Since every data point in a time series is a function of the previous time steps, the error in predicting the future time series data points compounds over time. To avoid prediction error, the correlation matrix of the time series is preferred over the direct prediction of the future time series data points. A correlation matrix of a given multivariate time series data set is useful in several practical real-world scenarios  \nEmail addresses: [sarika@iiti.ac.in](sarika@iiti.ac.in) (Sarika Jalan), [priodyutipradhan@gmail.com](priodyutipradhan@gmail.com) (Priodyuti Pradhan)  \nPreprint submitted to Journal of LATEX Templates October 10, 2022  \n[7, 8] . For instance, by considering fMRI or MEG signals from several brain regions as time series data, one can construct the corresponding correlation matrix, which can then be used to extract the adjacency matrix by setting a threshold value [9, 10, 11] .  \nIn most cases, one calculates average correlation matrices of a given time series data. A correlation matrix of time series data may vary depending on the length of observations and temporal position. Two well-known methods of estimating a true correlation matrix are; (i) the maximum likelihood estimation (MLE) and (ii) the graphical least absolute shrinkage and selection operator method (GLASSO) . The MLE method 􀀌rst assumes a sample correlation matrix from a Gaussian distribution that is iteratively corrected to estimate an actual correlation matrix by maximizing the likelihood of observing the given time series data. The GLASSO method","cbCaidbO7Qqs9B4A","https://ap.wps.com/l/cbCaidbO7Qqs9B4A","pdf",593449,1,13,"English","en",105,"# Introduction\n## Motivation and limitations of direct time-series prediction\n## Correlation matrix estimation methods (MLE and GLASSO)\n## Proposed machine learning framework\n# Preliminary\n## Graph/network setup\n# Model overview\n## Dynamical and machine learning models\n## Time-series generation and validation","[{\"question\":\"Why is predicting correlation matrices preferable to directly forecasting future time-series values?\",\"answer\":\"Because errors compound when forecasting future time points iteratively. Using the correlation matrix avoids the need for direct long-horizon prediction of future values.\"},{\"question\":\"What is the key contribution of the proposed machine learning approach?\",\"answer\":\"A supervised machine learning framework reconstructs the full correlation matrix from limited time-series data collected from only a few randomly selected nodes, instead of requiring all nodes.\"},{\"question\":\"How do the authors analyze and validate the prediction results?\",\"answer\":\"They evaluate mean square error between true and predicted correlation matrices, use an unsupervised algorithm (UMAP) for insight into the forecasts, and validate on real-world EEG datasets.\"}]","Estimation of Correlation Matrices from Limited Time Series Data using Machine Learning | PDF",1785811626,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"estimation-of-correlation-matrices-from-limited-time-series-data-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/estimation-of-correlation-matrices-from-limited-time-series-data-using-machine-learning/122591/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting correlation matrices preferable to directly forecasting future time-series values?","Question",{"text":75,"@type":76},"Because errors compound when forecasting future time points iteratively. Using the correlation matrix avoids the need for direct long-horizon prediction of future values.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the key contribution of the proposed machine learning approach?",{"text":80,"@type":76},"A supervised machine learning framework reconstructs the full correlation matrix from limited time-series data collected from only a few randomly selected nodes, instead of requiring all nodes.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the authors analyze and validate the prediction results?",{"text":84,"@type":76},"They evaluate mean square error between true and predicted correlation matrices, use an unsupervised algorithm (UMAP) for insight into the forecasts, and validate on real-world EEG datasets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]