{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/time-series/papers/66","list_of":"/task/time-series","task":"Time Series Analysis","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":66,"pages_in_order":68,"rows_per_page":100,"rows":[6501,6600],"of":6748,"counts":{"archive_papers_tagged":6748,"with_a_code_link":1993,"where_syntology_ran_a_sample":383,"not_listed_spam_title":0,"listed":6748,"listed_where_code_ran":383,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":327,"every_run_a_failure_of_syntologys_instrument":56,"listed_with_a_run_with_no_instrument_failure":327,"listed_every_run_a_failure_of_syntologys_instrument":56,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/time-series","prev":"/task/time-series/papers/65","next":"/task/time-series/papers/67","papers":[{"url":null,"slug":"optimal-time-series-motifs","title":"Optimal Time-Series Motifs","date":"2015-05-03","arxiv_id":"1505.00423","repositories_listed":0,"syntology":null},{"url":null,"slug":"market-forecasting-using-hidden-markov-models","title":"Market forecasting using Hidden Markov Models","date":"2015-04-29","arxiv_id":"1504.07829","repositories_listed":0,"syntology":null},{"url":null,"slug":"note-on-equivalence-between-recurrent-neural","title":"Note on Equivalence Between Recurrent Neural Network Time Series Models and Variational Bayesian Models","date":"2015-04-29","arxiv_id":"1504.08025","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-recurrent-neural-networks-for","title":"Differential Recurrent Neural Networks for Action Recognition","date":"2015-04-25","arxiv_id":"1504.06678","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-approach-for-structure-learning-in","title":"A Bayesian approach for structure learning in oscillating regulatory networks","date":"2015-04-24","arxiv_id":"1504.06553","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-new-approach-for-physiological-time-series","title":"A new approach for physiological time series","date":"2015-04-23","arxiv_id":"1504.06274","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-learning-algorithm-for-time-series","title":"Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes","date":"2015-04-21","arxiv_id":"1504.05517","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-trends-with-asset-prices","title":"Forecasting trends with asset prices","date":"2015-04-20","arxiv_id":"1504.03934","repositories_listed":0,"syntology":null},{"url":null,"slug":"estimating-the-algorithmic-complexity-of","title":"Estimating the Algorithmic Complexity of Stock Markets","date":"2015-04-16","arxiv_id":"1504.04296","repositories_listed":0,"syntology":null},{"url":null,"slug":"detrended-partial-cross-correlation-analysis","title":"Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces","date":"2015-04-15","arxiv_id":"1504.02435","repositories_listed":0,"syntology":null},{"url":null,"slug":"bidirectional-recurrent-neural-networks-as","title":"Bidirectional Recurrent Neural Networks as Generative Models - Reconstructing Gaps in Time Series","date":"2015-04-07","arxiv_id":"1504.01575","repositories_listed":0,"syntology":null},{"url":null,"slug":"eliciting-disease-data-from-wikipedia","title":"Eliciting Disease Data from Wikipedia Articles","date":"2015-04-02","arxiv_id":"1504.00657","repositories_listed":0,"syntology":null},{"url":null,"slug":"structure-learning-of-partitioned-markov","title":"Structure Learning of Partitioned Markov Networks","date":"2015-04-02","arxiv_id":"1504.00624","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-plus-low-rank-autoregressive","title":"Sparse plus low-rank autoregressive identification in neuroimaging time series","date":"2015-03-30","arxiv_id":"1503.08639","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-bayesian-approach-to-sparse-plus-low-rank","title":"A Bayesian Approach to Sparse plus Low rank Network Identification","date":"2015-03-25","arxiv_id":"1503.07340","repositories_listed":0,"syntology":null},{"url":null,"slug":"fusing-continuous-valued-medical-labels-using","title":"Fusing Continuous-valued Medical Labels using a Bayesian Model","date":"2015-03-23","arxiv_id":"1503.06619","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-based-action-recognition-using-rate","title":"Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories","date":"2015-03-23","arxiv_id":"1503.06699","repositories_listed":0,"syntology":null},{"url":null,"slug":"asymmetric-distributions-from-constrained","title":"Asymmetric Distributions from Constrained Mixtures","date":"2015-03-22","arxiv_id":"1503.06429","repositories_listed":0,"syntology":null},{"url":null,"slug":"ultra-fast-shapelets-for-time-series","title":"Ultra-Fast Shapelets for Time Series Classification","date":"2015-03-17","arxiv_id":"1503.05018","repositories_listed":0,"syntology":null},{"url":null,"slug":"scalable-discovery-of-time-series-shapelets","title":"Scalable Discovery of Time-Series Shapelets","date":"2015-03-11","arxiv_id":"1503.03238","repositories_listed":0,"syntology":null},{"url":null,"slug":"ranking-and-significance-of-variable-length","title":"Ranking and significance of variable-length similarity-based time series motifs","date":"2015-03-06","arxiv_id":"1503.01883","repositories_listed":0,"syntology":null},{"url":null,"slug":"low-dimensional-models-in-spatio-temporal","title":"Low-dimensional Models in Spatio-Temporal Wind Speed Forecasting","date":"2015-03-04","arxiv_id":"1503.01210","repositories_listed":0,"syntology":null},{"url":null,"slug":"telling-cause-from-effect-in-deterministic","title":"Telling cause from effect in deterministic linear dynamical systems","date":"2015-03-04","arxiv_id":"1503.01299","repositories_listed":0,"syntology":null},{"url":null,"slug":"signal-processing-on-graphs-causal-modeling","title":"Signal Processing on Graphs: Causal Modeling of Unstructured Data","date":"2015-02-28","arxiv_id":"1503.00173","repositories_listed":0,"syntology":null},{"url":null,"slug":"iteratively-reweighted-adaptive-lasso-for","title":"Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes","date":"2015-02-23","arxiv_id":"1502.06557","repositories_listed":0,"syntology":null},{"url":null,"slug":"contour-map-of-estimation-error-for-expected","title":"Contour map of estimation error for Expected Shortfall","date":"2015-02-22","arxiv_id":"1502.06217","repositories_listed":0,"syntology":null},{"url":null,"slug":"real-time-clustering-of-time-series-using","title":"Real time clustering of time series using triangular potentials","date":"2015-02-18","arxiv_id":"1502.05090","repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-embedding-in-convolutional-neural","title":"Temporal Embedding in Convolutional Neural Networks for Robust Learning of Abstract Snippets","date":"2015-02-18","arxiv_id":"1502.05113","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-gradient-learning-on-time-series","title":"Generalized Gradient Learning on Time Series under Elastic Transformations","date":"2015-02-17","arxiv_id":"1502.04843","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-transfer-function-nonlinearity-in","title":"Exploring Transfer Function Nonlinearity in Echo State Networks","date":"2015-02-16","arxiv_id":"1502.04423","repositories_listed":0,"syntology":null},{"url":null,"slug":"dependent-matern-processes-for-multivariate","title":"Dependent Matérn Processes for Multivariate Time Series","date":"2015-02-11","arxiv_id":"1502.03466","repositories_listed":0,"syntology":null},{"url":null,"slug":"variable-and-fixed-interval-exponential","title":"Variable and Fixed Interval Exponential Smoothing","date":"2015-02-11","arxiv_id":"1502.03465","repositories_listed":0,"syntology":null},{"url":null,"slug":"product-reservoir-computing-time-series","title":"Product Reservoir Computing: Time-Series Computation with Multiplicative Neurons","date":"2015-02-03","arxiv_id":"1502.00718","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-ordinal-pattern-approach-to-detect-and-to","title":"An Ordinal Pattern Approach to Detect and to Model Leverage Effects and Dependence Structures Between Financial Time Series","date":"2015-01-29","arxiv_id":"1502.07321","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpreting-16s-metagenomic-data-without","title":"Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution","date":"2015-01-29","arxiv_id":"1312.0570","repositories_listed":0,"syntology":null},{"url":null,"slug":"particle-swarm-optimization-for-time-series","title":"Particle swarm optimization for time series motif discovery","date":"2015-01-29","arxiv_id":"1501.07399","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-search-for-candidate-relevant-subsets-of","title":"The search for candidate relevant subsets of variables in complex systems","date":"2015-01-27","arxiv_id":"1502.01734","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-western-africa-ebola-virus-disease","title":"The Western Africa Ebola virus disease epidemic exhibits both global exponential and local polynomial growth rates","date":"2015-01-27","arxiv_id":"1411.7364","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-spectral-method-for-inferring-general","title":"A novel spectral method for inferring general diploid selection from time series genetic data","date":"2015-01-26","arxiv_id":"1310.1068","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-manipulation-detection-via-permutation","title":"Data manipulation detection via permutation information theory quantifiers","date":"2015-01-16","arxiv_id":"1501.04123","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatiotemporal-clustering-climate-periodicity","title":"Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010","date":"2015-01-13","arxiv_id":"1407.7913","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-of-the-l2-boost-technique","title":"An Empirical Study of the L2-Boost technique with Echo State Networks","date":"2015-01-02","arxiv_id":"1501.00503","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficiently-discovering-frequent-motifs-in","title":"Efficiently Discovering Frequent Motifs in Large-scale Sensor Data","date":"2015-01-02","arxiv_id":"1501.00405","repositories_listed":0,"syntology":null},{"url":null,"slug":"reasoning-for-improved-sensor-data","title":"Reasoning for Improved Sensor Data Interpretation in a Smart Home","date":"2014-12-26","arxiv_id":"1412.7961","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-modeling-of-hidden-functional","title":"Generative Modeling of Hidden Functional Brain Networks","date":"2014-12-20","arxiv_id":"1412.6602","repositories_listed":0,"syntology":null},{"url":null,"slug":"reverse-engineering-chemical-reaction","title":"Reverse Engineering Chemical Reaction Networks from Time Series Data","date":"2014-12-19","arxiv_id":"1412.6346","repositories_listed":0,"syntology":null},{"url":null,"slug":"ann-model-to-predict-stock-prices-at-stock","title":"ANN Model to Predict Stock Prices at Stock Exchange Markets","date":"2014-12-17","arxiv_id":"1502.06434","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-vector-autoregression","title":"High Dimensional Forecasting via Interpretable Vector Autoregression","date":"2014-12-17","arxiv_id":"1412.5250","repositories_listed":0,"syntology":null},{"url":null,"slug":"subspace-based-low-rank-and-joint-sparse","title":"Subspace based low rank and joint sparse matrix recovery","date":"2014-12-05","arxiv_id":"1412.2700","repositories_listed":0,"syntology":null},{"url":null,"slug":"highly-comparative-fetal-heart-rate-analysis","title":"Highly comparative fetal heart rate analysis","date":"2014-12-03","arxiv_id":"1412.1138","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-brain-states-from-multi-region","title":"Analysis of Brain States from Multi-Region LFP Time-Series","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"coresets-for-k-segmentation-of-streaming-data","title":"Coresets for k-Segmentation of Streaming Data","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"covariance-shrinkage-for-autocorrelated-data","title":"Covariance shrinkage for autocorrelated data","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"how-to-monitor-and-mitigate-stair-casing-in","title":"How to monitor and mitigate stair-casing in l1 trend filtering","date":"2014-12-01","arxiv_id":"1412.0607","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-convolution-filters-for-inverse","title":"Learning convolution filters for inverse covariance estimation of neural network connectivity","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"modeling-deep-temporal-dependencies-with","title":"Modeling Deep Temporal Dependencies with Recurrent Grammar Cells\"\"","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-graphical-models-for-spatio","title":"Multi-scale Graphical Models for Spatio-Temporal Processes","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"on-line-summarization-of-time-series","title":"On-line Summarization of Time-series Documents using a Graph-based Algorithm","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"tree-structured-gaussian-process","title":"Tree-structured Gaussian Process Approximations","date":"2014-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"clustering-evolving-data-using-kernel-based","title":"Clustering evolving data using kernel-based methods","date":"2014-11-20","arxiv_id":"1411.5988","repositories_listed":0,"syntology":null},{"url":null,"slug":"causal-inference-by-identification-of-vector","title":"Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components","date":"2014-11-14","arxiv_id":"1411.3972","repositories_listed":0,"syntology":null},{"url":null,"slug":"statistically-significant-detection-of","title":"Statistically Significant Detection of Linguistic Change","date":"2014-11-12","arxiv_id":"1411.3315","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-short-image-series-based-scheme-for-time","title":"A Short Image Series Based Scheme for Time Series Digital Image Correlation","date":"2014-10-28","arxiv_id":"1410.7613","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-function-to-function-regression","title":"Fast Function to Function Regression","date":"2014-10-27","arxiv_id":"1410.7414","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-output-regression-with-latent-noise","title":"Multiple Output Regression with Latent Noise","date":"2014-10-27","arxiv_id":"1410.7365","repositories_listed":0,"syntology":null},{"url":null,"slug":"initialization-of-multilayer-forecasting","title":"Initialization of multilayer forecasting artifical neural networks","date":"2014-10-23","arxiv_id":"1410.6413","repositories_listed":0,"syntology":null},{"url":null,"slug":"graphical-lasso-based-model-selection-for","title":"Graphical LASSO Based Model Selection for Time Series","date":"2014-10-05","arxiv_id":"1410.1184","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-algorithm-for-neuro-fuzzy-network","title":"Training Algorithm for Neuro-Fuzzy Network Based on Singular Spectrum Analysis","date":"2014-10-05","arxiv_id":"1410.1151","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploiting-social-relations-and-sentiment-for","title":"Exploiting Social Relations and Sentiment for Stock Prediction","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"financial-keyword-expansion-via-continuous","title":"Financial Keyword Expansion via Continuous Word Vector Representations","date":"2014-10-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-multivariate-sequence","title":"Efficient multivariate sequence classification","date":"2014-09-29","arxiv_id":"1409.8211","repositories_listed":0,"syntology":null},{"url":null,"slug":"when-darwin-meets-lorenz-evolving-new-chaotic","title":"When Darwin meets Lorenz: Evolving new chaotic attractors through genetic programming","date":"2014-09-27","arxiv_id":"1409.7842","repositories_listed":0,"syntology":null},{"url":null,"slug":"short-term-solar-irradiance-and-irradiation","title":"Short-term solar irradiance and irradiation forecasts via different time series techniques: A preliminary study","date":"2014-09-26","arxiv_id":"1409.7476","repositories_listed":0,"syntology":null},{"url":null,"slug":"metric-learning-for-temporal-sequence","title":"Metric Learning for Temporal Sequence Alignment","date":"2014-09-10","arxiv_id":"1409.3136","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-sensor-event-detection-using-shape","title":"Multi-Sensor Event Detection using Shape Histograms","date":"2014-08-16","arxiv_id":"1408.3733","repositories_listed":0,"syntology":null},{"url":null,"slug":"automated-machine-learning-on-big-data-using","title":"Automated Machine Learning on Big Data using Stochastic Algorithm Tuning","date":"2014-07-30","arxiv_id":"1407.7969","repositories_listed":0,"syntology":null},{"url":null,"slug":"dissimilarity-based-sparse-subset-selection","title":"Dissimilarity-based Sparse Subset Selection","date":"2014-07-25","arxiv_id":"1407.6810","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-recurrent-neural-networks-for-time","title":"Deep Recurrent Neural Networks for Time Series Prediction","date":"2014-07-22","arxiv_id":"1407.5949","repositories_listed":0,"syntology":null},{"url":null,"slug":"one-step-or-two-step-optimization-and-the","title":"One-Step or Two-Step Optimization and the Overfitting Phenomenon: A Case Study on Time Series Classification","date":"2014-07-16","arxiv_id":"1407.4364","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-exploring-non-linear","title":"A Framework for Exploring Non-Linear Functional Connectivity and Causality in the Human Brain: Mutual Connectivity Analysis (MCA) of Resting-State Functional MRI with Convergent Cross-Mapping and Non-Metric Clustering","date":"2014-07-14","arxiv_id":"1407.3809","repositories_listed":0,"syntology":null},{"url":null,"slug":"finding-motif-sets-in-time-series","title":"Finding Motif Sets in Time Series","date":"2014-07-14","arxiv_id":"1407.3685","repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-cover-songs-using-information","title":"Identifying Cover Songs Using Information-Theoretic Measures of Similarity","date":"2014-07-09","arxiv_id":"1407.2433","repositories_listed":0,"syntology":null},{"url":null,"slug":"meteorological-time-series-forecasting-with","title":"Meteorological time series forecasting with pruned multi-layer perceptron and 2-stage Levenberg-Marquardt method","date":"2014-07-08","arxiv_id":"1407.2169","repositories_listed":0,"syntology":null},{"url":null,"slug":"techniques-for-clustering-interaction-data-as","title":"Techniques for clustering interaction data as a collection of graphs","date":"2014-06-24","arxiv_id":"1406.6319","repositories_listed":0,"syntology":null},{"url":null,"slug":"early-recognition-of-human-activities-from","title":"Early Recognition of Human Activities from First-Person Videos Using Onset Representations","date":"2014-06-20","arxiv_id":"1406.5309","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-experimental-evaluation-of-nearest","title":"An Experimental Evaluation of Nearest Neighbour Time Series Classification","date":"2014-06-18","arxiv_id":"1406.4757","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-gaussian-process-state-space","title":"Variational Gaussian Process State-Space Models","date":"2014-06-18","arxiv_id":"1406.4905","repositories_listed":0,"syntology":null},{"url":null,"slug":"interval-forecasting-of-electricity-demand-a","title":"Interval Forecasting of Electricity Demand: A Novel Bivariate EMD-based Support Vector Regression Modeling Framework","date":"2014-06-15","arxiv_id":"1406.3792","repositories_listed":0,"syntology":null},{"url":null,"slug":"dimensionality-reduction-for-time-series-data","title":"Dimensionality reduction for time series data","date":"2014-06-14","arxiv_id":"1406.3711","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hybrid-neuro-wavelet-predictor-for-qos","title":"A hybrid neuro--wavelet predictor for QoS control and stability","date":"2014-06-12","arxiv_id":"1406.3156","repositories_listed":0,"syntology":null},{"url":null,"slug":"variational-inference-of-latent-state","title":"Variational inference of latent state sequences using Recurrent Networks","date":"2014-06-06","arxiv_id":"1406.1655","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-classification-based-stock","title":"Supervised classification-based stock prediction and portfolio optimization","date":"2014-06-03","arxiv_id":"1406.0824","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparing-multi-label-classification-with","title":"Comparing Multi-label Classification with Reinforcement Learning for Summarisation of Time-series Data","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-adaptive-natural-language-generation","title":"Multi-adaptive Natural Language Generation using Principal Component Regression","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"recognition-of-complex-events-exploiting","title":"Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-event-coreference-resolution","title":"Unsupervised Event Coreference Resolution","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-conceptual-spaces-to-model-domain","title":"Using Conceptual Spaces to Model Domain Knowledge in Data-to-Text Systems","date":"2014-06-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"an-ordered-lasso-and-sparse-time-lagged","title":"An Ordered Lasso and Sparse Time-Lagged Regression","date":"2014-05-26","arxiv_id":"1405.6447","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-bayesian-modeling-of-groups-of","title":"Effective Bayesian Modeling of Groups of Related Count Time Series","date":"2014-05-15","arxiv_id":"1405.3738","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-deep-fourier-neural-networks-to-fit","title":"Training Deep Fourier Neural Networks To Fit Time-Series Data","date":"2014-05-09","arxiv_id":"1405.2262","repositories_listed":0,"syntology":null}],"record_sha256":"a0a08ad4bc1f4e964da00fffc74d7ca19badbd0bae75c5cc2e83dc73298bedbe","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}