Academic literature on the topic 'Multivariate categorical time-series'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Multivariate categorical time-series.'
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 "Multivariate categorical time-series"
1
Davis, Ginger M., and Katherine B. Ensor. "Multivariate Time-Series Analysis With Categorical and Continuous Variables in an Lstr Model." Journal of Time Series Analysis 28, no. 6 (November 2007): 867–85. http://dx.doi.org/10.1111/j.1467-9892.2007.00537.x.
Full text
2
Xu, Dongting, Zhisheng Zhang, and Jinfei Shi. "Training Data Selection by Categorical Variables for Better Rare Event Prediction in Multiple Products Production Line." Electronics 11, no. 7 (March 28, 2022): 1056. http://dx.doi.org/10.3390/electronics11071056.
Full textAbstract:
Manufacturers are struggling to use data from multiple products production lines to predict rare events. Improving the quality of training data is a common way to improve the performance of algorithms. However, there is little research about how to select training data quantitatively. In this study, a training data selection method is proposed to improve the performance of deep learning models. The proposed method can represent different time length multivariate time series spilt by categorical variables and measure the (dis)similarities by the distance matrix and clustering method. The contributions are: (1) The proposed method can find the changes to the training data caused by categorical variables in a multivariate time series dataset; (2) according to the proposed method, the multivariate time series data from the production line can be clustered into many small training datasets; and (3) same structure but different parameters prediction models are built instead of one model which is different from the traditional way. In practice, the proposed method is applied in a real multiple products production line dataset and the result shows it can not only significantly improve the performance of the reconstruction model but it can also quantitively measure the (dis)similarities of the production behaviors.
3
Xue, Feng, Weizhong Yan, Tianyi Wang, Hao Huang, and Bojun Feng. "Deep anomaly detection for industrial systems: a case study." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 8. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1186.
Full textAbstract:
We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation is used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are categorical in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.
4
Tew-Kai, Emilie, Victor Quilfen, Marie Cachera, and Martial Boutet. "Dynamic Coastal-Shelf Seascapes to Support Marine Policies Using Operational Coastal Oceanography: The French Example." Journal of Marine Science and Engineering 8, no. 8 (August 5, 2020): 585. http://dx.doi.org/10.3390/jmse8080585.
Full textAbstract:
In the context of maritime spatial planning and the implementation of spatialized Good Environmental Status indicators in the Marine Strategy Framework Directive (MSFD), the definition of a mosaic composed of coherent and standardised spatial units is necessary. We propose here a characterization of seascapes in time and space within the specific framework of the MSFD in the English Channel and the Bay of Biscay areas. A spatio-temporal classification of coastal-shelf water masses is carried out using twelve essential oceanographic and derived variables from operational coastal oceanography using the HYCOM model. Partitioning is computed using a multivariate hybrid two-step clustering process defining a time series of categorical maps representing hydrographical patch classes. Main patch occurrence is analyzed to understand their spatio-temporal dynamics and their oceanographic characteristics. Finally, patch classes are combined with MSFD marine sub-region delimitations to build seascapes, including ecosystem approach management and marine policy considerations.
5
Maravelakis, Petros. "The use of statistics in social sciences." Journal of Humanities and Applied Social Sciences 1, no. 2 (November 15, 2019): 87–97. http://dx.doi.org/10.1108/jhass-08-2019-0038.
Full textAbstract:
Purpose The purpose this paper is to review some of the statistical methods used in the field of social sciences. Design/methodology/approach A review of some of the statistical methodologies used in areas like survey methodology, official statistics, sociology, psychology, political science, criminology, public policy, marketing research, demography, education and economics. Findings Several areas are presented such as parametric modeling, nonparametric modeling and multivariate methods. Focus is also given to time series modeling, analysis of categorical data and sampling issues and other useful techniques for the analysis of data in the social sciences. Indicative references are given for all the above methods along with some insights for the application of these techniques. Originality/value This paper reviews some statistical methods that are used in social sciences and the authors draw the attention of researchers on less popular methods. The purpose is not to give technical details and also not to refer to all the existing techniques or to all the possible areas of statistics. The focus is mainly on the applied aspect of the techniques and the authors give insights about techniques that can be used to answer problems in the abovementioned areas of research.
6
Lin, Luotao, Jiaqi Guo, Marah Aqeel, Anindya Bhadra, Saul Gelfand, Edward Delp, Elizabeth Richards, Erin Hennessy, and Heather Eicher-Miller. "Temporal Patterning Integrating Diet and Physical Activity Shows Stronger Links to Health Indicators Compared to Patterning of Either Diet or Physical Activity Alone." Current Developments in Nutrition 5, Supplement_2 (June 2021): 469. http://dx.doi.org/10.1093/cdn/nzab039_005.
Full textAbstract:
Abstract Objectives Daily temporal patterns of energy intake (temporal dietary patterns, TDPs) and physical activity (temporal PA patterns, TPAPs) have been independently and jointly (joint temporal dietary and PA patterns, TDPAPs) associated with health indicators. The strength of the association between clusters of each pattern and health indicators including body mass index (BMI), waist circumference (WC), fasting plasma glucose (FPG), hemoglobin A1c (A1c), triglyceride (TAG), high-density lipoprotein cholesterol (HDL-C), total cholesterol (Total-C), blood pressure, type 2 diabetes (T2D), metabolic syndrome (MetS), and obesity, were compared. Methods The reported energy throughout a day from one reliable 24-hour weekday dietary recall and activity counts from a random weekday of PA accelerometer data of 1,836 U.S. adults from the National Health and Nutrition Examination Survey (2003–2006) were used to create TDP and TPAP respectively, and jointly for TDPAP. Constrained dynamic time warping distances computed over the time series were partitioned to four clusters using kernel-k means clustering algorithm. Measured BMI, WC, FPG, A1c, TAG, HDL-C, Total-C, and classified T2DM, MetS, and obesity were outcomes in multivariate regression models to determine associations with the clusters representing each pattern, controlling for potential confounders and adjusting for multiple comparisons (P < 0.05/6). Adjusted R2 and Akaike information criterion (AIC) compared the strength of the associations between clusters and continuous or categorical health indicators. Results All temporal patterns were significantly associated with BMI, WC, and obesity. Adjusted R2 of BMI and WC models for significant predictors’ effects were higher for TDPAPs (0.129 and 0.194) than TDPs (0.117 and 0.186) or TPAPs (0.077 and 0.143), and AIC of obesity for the TDPAPs (234,752,082) was smaller than for TDPs (236,650,170) or TPAPs (239,810,423). Conclusions TDPAPs incorporating time of day with energy intake and PA had the strongest associations with BMI, WC, and obesity compared with either independent temporal dietary or PA patterns. Patterns representing the integration of multiple daily behavioral habits hold promise for early detection of obesity. Funding Sources NIH (R21CA224764) and Purdue University.
7
Knapik, Derrick M., Ian M. Clapp, Daniel Wichman, and Shane J. Nho. "Use of Younger Patient Age and Greater Anterior Center-Edge Angle to Predict the Need for Bilateral Hip Arthroscopy in Patients With Bilateral Femoroacetabular Impingement–Related Hip Pain." American Journal of Sports Medicine 49, no. 8 (June 3, 2021): 2110–16. http://dx.doi.org/10.1177/03635465211015431.
Full textAbstract:
Background: In patients with symptomatic femoroacetabular impingement syndrome, bilateral hip pain has been reported to occur in high frequency. However, not all patients require bilateral hip arthroscopy. Purpose: To determine the incidence, patient-specific variables, and postoperative outcomes in patients who presented with bilateral hip pain at the time of index hip arthroscopy and underwent subsequent contralateral arthroscopic hip surgery. Study Design: Case series; Level of evidence, 4. Methods: Patients who presented with bilateral hip pain, underwent primary hip arthroscopy between January 2012 and June 2018 for indication of femoroacetabular impingement syndrome, and had minimum 2-year follow-up were retrospectively analyzed. Baseline descriptive data, preoperative hip range of motion, and radiographic measurements were recorded with pre- and postoperative patient-reported outcomes (PROs). Independent samples t test was used to compare continuous variables, and chi-square test was used to compare categorical variables between patients undergoing unilateral and bilateral surgery. Bivariate correlations and a multivariable binary logistic regression were performed to determine factors predictive of the need for future contralateral hip arthroscopy. Results: In total, 108 patients were identified who reported bilateral hip pain during the index evaluation, underwent primary hip arthroscopy, and had 2-year follow-up. Among these, 42% (n = 45) elected to undergo hip arthroscopy on the contralateral hip at a mean of 6.0 months (range, 1-17 months) after the index surgery. Patients requiring bilateral surgery were significantly younger ( P = .004) and had a larger preoperative anterior center-edge angle (ACEA; P = .038) when compared with patients who had unilateral surgery. There were no significant differences in alpha angle measurements between patients who had unilateral and bilateral surgery. On bivariate analysis, younger age at the time of the index surgery ( r = −0.272; P = .005) and preoperative ACEA ( r = 0.249; P = .016) were significantly correlated with the need for bilateral surgery. On multivariate analysis, younger age remained a significant predictor for bilateral surgery (odds ratio, 0.95; 95% CI, 0.91-0.99). Patients who underwent bilateral hip arthroscopy reported significant improvement in all PROs ( P < .001), with a significantly greater mean Hip Outcome Score− Sports Specific Subscale score when compared with patients undergoing unilateral surgery ( P = .037). Conclusion: Subsequent contralateral hip arthroscopy was performed in 42% of patients who presented with bilateral hip pain. Younger age at the time of the index surgery and greater ACEA were predictive of the need for contralateral surgery. Patients undergoing bilateral surgery reported significantly improvement in PROs at minimum 2-year follow-up.
8
Hasegawa, Daiichiro, Satoshi Hirase, Hironobu Takahashi, Atsuro Saito, Aiko Kozaki, Toshiaki Ishida, Tomoko Yanai, et al. "Absolute Lymphocyte Counts at the End of Induction Is a Prognostic Indicator in Childhood Acute Lymphoblastic Leukemia." Blood 124, no. 21 (December 6, 2014): 2264. http://dx.doi.org/10.1182/blood.v124.21.2264.2264.
Full textAbstract:
Abstract BACKGROUND: Recently, several studies have demonstrated that absolute lymphocyte counts (ALC) after induction therapy predicted treatment outcome. To address this issue, we here assessed the impact of the ALC at the end of induction therapy on outcomes in childhood ALL. METHODS: We reviewed 141 cases of pediatric ALL with 1-21 years of age treated on the Japan Association Childhood Leukemia Study group ALL-02 series of treatment trials between 2002 and 2013. Patients with Philadelphia chromosome-positive ALL were excluded. Variables retrospectively analyzed included ALC at several time points during remission induction, age at diagnosis, gender, initial white blood cell count (WBC), cytogenetics, immunological phenotype, stratified risk, treatment response for bone marrow (the percentage of blasts at day 15), and outcome. Events in the analysis of event-free survival (EFS) included induction failure, death, relapse and secondary malignant neoplasm. The comparison of categorical variables between groups was performed by chi-square test. The probability of EFS and overall survival (OS) were analyzed with the use of the Kaplan–Meier method and a stratified log-rank test. A multivariate analysis of survival was performed with the use of a Cox proportional-hazard model to evaluate the treatment effect with adjustment for stratification factors. RESULTS: The subjects included 121 of B-precursor ALL, 10 of T cell ALL, and 4 of acute mixed lineage leukemia/ acute unclassified leukemia. We found high WBC count at diagnosis (>100K/microL) and slow early responder for bone marrow at day 15 to be an unfavorable prognostic indicator, and also the ALC at the end of induction (day29) to be a statistically significant predictor of improved OS and EFS in our cohort. Patients with ALC ≥ 800/microL had a superior 5-year overall survival (100 ± 1.7% vs 88.1 ± 4.3 %, p=0.0001) and EFS (98.3 ± 1.7% vs 81.8 ± 5.0 %, p=0.0001). Multivariate analysis demonstrated that ALC at day29 was an independent, clinically significant predictor of improved EFS and OS after controlling WBC at diagnosis, gender, age at diagnosis, and cytogenetics. Multiple regression analysis adjusting for initial WBC count, peripheral blast counts at day8, and cytogenetics, also revealed an independent relationship (p=0.005) between treatment response (the percentage of blasts at day 15) and ALC at day29. CONCLUSIONS: ALC is a simple, statistically significant prognostic factor in childhood ALL that may refine current risk stratification algorithms. Disclosures No relevant conflicts of interest to declare.
9
Lone, Zaeem M., Tarik Benidir, Magdalena Rainey, Monica Nair, Elai Davicioni, Ewan Gibb, Sean Williamson, et al. "A genomic classifier for prostate cancer correlates with adverse pathologic features: Transcriptomic features of cribriform and intraductal carcinoma of the prostate." Journal of Clinical Oncology 40, no. 6_suppl (February 20, 2022): 268. http://dx.doi.org/10.1200/jco.2022.40.6_suppl.268.
Full textAbstract:
268 Background: Invasive cribriform and intraductal carcinoma (CF/IDC) portends an unfavorable prognosis for patients diagnosed with prostate cancer (CaP). Limited studies with small sample sizes have explored whether genomic classifiers are associated with IDC and/or CF status. We investigated the correlation between Decipher genomic risk score and IDC/CF status and assessed PCa transcriptomic features. Methods: We performed a retrospective review of CaP patients who had Decipher testing at a single high volume center between 2009-2020. The highest grade index lesion from radical prostatectomy specimens was identified by GU pathologists and used for Decipher testing. Genitourinary pathologists reviewed prostatectomy specimens for the presence of CF and IDC features. Patients were divided into three groups based on pathologic features, absent CF/IDC (CF-/IDC-), CF positive only (CF+/IDC-), and CF/IDC positive (CF+/IDC+). Categorical clinical, genomic, and pathologic variables were assessed using the Pearson Chi-Square test, quantitative with the Kruskal-Wallis test. Multivariable logistic regression was used to identify predictors of high-risk Decipher GC scores. The Kaplan-Meier method with log-rank was used to compare biochemical recurrence free survival. Differential gene expression and gene network analysis was used to identify genes and pathways associated with IDC/CF features. Results: 463 patients were included with a median follow-up of 25 months. Patients who were CF+/IDC+ had higher GC scores (CF+/IDC+: 0.77 vs. CF+/IDC-: 0.71 vs. CF-/IDC-: 0.61, p<0.001). Patients who were CF+/IDC+ had a higher percentage of Gleason grade group >3 (CF+/IDC+: 79% vs. CF+/IDC-: 52% vs. CF-/IDC-: 52%, p<0.001). On multivariate logistic regression, predictors of high-risk GC score were presence of CF+/IDC+ features on final pathology (OR: 3.94, p<0.001) and pathologic Gleason grade group >3 (OR: 1.58, p=0.04). Transcriptomic analysis revealed that the hallmark androgen response pathway was significantly upregulated in CF+/IDC+ patients (Log fold change: 15.7, p<0001). Conclusions: This is the largest series investigating the association of a clinically validated genomic classifier and pathologic features such as cribriform and intraductal carcinoma. These findings have implications for the use of genomic classifiers in settings where expert GU pathology is not readily available and in potentially unmasking adverse histology at the time of biopsy.[Table: see text]
10
Mondaca, Sebastian, Henry S. Walch, Subhiksha Nandakumar, Walid K. Chatila, Jaclyn Frances Hechtman, Andrea Cercek, Luis A. Diaz, et al. "Influence of WNT and DNA damage response pathway alterations on outcomes in patients with unresectable metastatic colorectal cancer." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 3585. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.3585.
Full textAbstract:
3585 Background: We assembled a large series of consecutive patients with unresectable metastatic colorectal cancer (mCRC) to identify genomic biomarkers of response and survival. Methods: Patients with unresectable mCRC treated at Memorial Sloan Kettering with genomic tumor profiling between 2014 and 2017 were included. Patients who underwent upfront metastasectomy or received neoadjuvant/conversion chemotherapy were excluded. Clinical information was retrieved from electronic medical records, and we evaluated associations between genomic profiles with progression free survival (PFS) on first-line chemotherapy and overall survival (OS). Categorical data were analyzed by Fisher exact test and time-to-event data were analyzed by Cox proportional hazards models. Results: Of 1453 mCRCs profiled in this period, 471 patients met the study criteria. Median age was 59 years (range, 18 to 95), and 73% of patients were stage IV at diagnosis. Most tumors (91%) were microsatellite stable (MSS). The most frequent first-line regimen was FOLFOX +/- bevacizumab (66%). Among MSS patients treated with oxaliplatin-containing regimens (n = 305), 7% harbored alterations in genes associated with DNA damage response (DDR) (BRCA1, BRCA2, ATM, PALB2). DDR gene alterations were not associated with PFS (P = 0.94) nor were different quartiles of large-state transitions (P = 0.54). Genomic alterations that significantly varied by duration of response included BRAF (16%, 10%, and 5% for PFS < 6 months, 6-12 months, and > 12 months, respectively) and APC (62%, 74%, and 80% for PFS < 6 months, 6-12 months, and > 12 months, respectively). APC mutation, single or dual, was associated with significantly longer PFS (HR 0.67) and OS (HR 0.59) in multivariate analysis versus no WNT pathway alteration or alterations in other WNT pathway genes (RNF43, AXIN2, CTNNB1). Conclusions: In unresectable mCRC patients, mutations in APC were associated with better outcomes; absence of an APC alteration or the occurrence of other WNT pathway alterations was associated with shorter survival. Somatic alterations in DDR genes were not associated with outcomes in mCRC patients receiving oxaliplatin-containing regimen.
Dissertations / Theses on the topic "Multivariate categorical time-series"
1
Reichmann, Lena [Verfasser], and Carsten [Akademischer Betreuer] Jentsch. "Time series models for multivariate binary and categorical data / Lena Reichmann ; Betreuer: Carsten Jentsch." Mannheim : Universitätsbibliothek Mannheim, 2021. http://d-nb.info/1237618312/34.
Full text
2
MAQSOOD, RABIA. "ANALYZING AND MODELING STUDENTS¿ BEHAVIORAL DYNAMICS IN CONFIDENCE-BASED ASSESSMENT." Doctoral thesis, Università degli Studi di Milano, 2020. http://hdl.handle.net/2434/699383.
Full textAbstract:
Confidence-based assessment is a two-dimensional assessment paradigm which considers the confidence or expectancy level a student has about the answer, to ascertain his/her actual knowledge. Several researchers have discussed the usefulness of this model over the traditional one-dimensional assessment approach, which takes the number of correctly answered questions as a sole parameter to calculate the test scores of a student. Additionally, some educational psychologists and theorists have found that confidence-based assessment has a positive impact on students’ academic performance, knowledge retention, and metacognitive abilities of self-regulation and engagement depicted during a learning process. However, to the best of our knowledge, these findings are not exploited by the educational data mining community, aiming to exploit students (logged) data to investigate their performance and behavioral characteristics in order to enhance their performance outcomes and/or learning experiences. Engagement reflects a student’s active participation in an ongoing task or process, that becomes even more important when students are interacting with a computer-based learning or assessment system. There is some evidence that students’ online engagement (which is estimated through their behaviors while interacting with a learning/assessment environment) is also positively correlated with good performance scores. However, no data mining method to date has measured students engagement behaviors during confidence-based assessment. This Ph.D. research work aimed to identify, analyze, model and predict students’ dynamic behaviors triggered by their progression in a computer-based assessment system, offering confidence-driven questions. The data was collected from two experimental studies conducted with undergraduate students who solved a number of problems during confidence-based assessment. In this thesis, we first addressed the challenge of identifying different parameters representing students’ problem-solving behaviors that are positively correlated with confidence-based assessment. Next, we developed a novel scheme to classify students’ problem-solving activities into engaged or disengaged behaviors using the three previously identified parameters namely: students’ response correctness, confidence level, feedback seeking/no-seeking behavior. Our next challenge was to exploit the students’ interactions recorded at the micro-level, i.e. event by event, by the computer-based assessment tools, to estimate their intended engagement behaviors during the assessment. We also observed that traditional non-mixture, first-order Markov chain is inadequate to capture students’ evolving behaviors revealed from their interactions with a computer-based learning/assessment system. We, therefore, investigated mixture Markov models to map students trails of performed activities. However, the quality of the resultant Markov chains is critically dependent on the initialization of the algorithm, which is usually performed randomly. We proposed a new approach for initializing the Expectation-Maximization algorithm for multivariate categorical data we called K-EM. Our method achieved better prediction accuracy and convergence rate in contrast to two pre-existing algorithms when applied on two real datasets. This doctoral research work contributes to elevate the existing states of the educational research (i.e. theoretical aspect) and the educational data mining area (i.e. empirical aspect). The outcomes of this work pave the way to a framework for an adaptive confidence-based assessment system, contributing to one of the central components of Adaptive Learning, that is, personalized student models. The adaptive system can exploit data generated in a confidence-based assessment system, to model students’ behavioral profiles and provide personalized feedback to improve students’ confidence accuracy and knowledge by considering their behavioral dynamics.
Book chapters on the topic "Multivariate categorical time-series"
1
Lin, Yu-Feng, Hsuan-Hsu Chen, Vincent S. Tseng, and Jian Pei. "Reliable Early Classification on Multivariate Time Series with Numerical and Categorical Attributes." In Advances in Knowledge Discovery and Data Mining, 199–211. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18038-0_16.
Full text
2
LEWIS, PETER A. W., and BONNIE K. RAY. "NONLINEAR MODELING OF MULTIVARIATE AND CATEGORICAL TIME SERIES USING MULTIVARIATE ADAPTIVE REGRESSION SPLINES." In Dimension Estimation and Models, 136–69. WORLD SCIENTIFIC, 1993. http://dx.doi.org/10.1142/9789814317382_0003.
Full text
3
Biswas, Atanu, and Apratim Guha. "Time Series of Categorical Data Using Auto-Mutual Information with Application of Fitting an AR(2) Model." In Advances in Multivariate Statistical Methods, 421–35. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789812838247_0025.
Full text