SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

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Showing 1751–1800 of 6748 papers

TitleStatusHype
Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time SeriesCode0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
Capturing the temporal constraints of gradual patternsCode0
Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural NetworksCode0
End-to-End Learned Early Classification of Time Series for In-Season Crop Type MappingCode0
Capturing Structure Implicitly from Time-Series having Limited DataCode0
Capturing Actionable Dynamics with Structured Latent Ordinary Differential EquationsCode0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
Elastic bands across the path: A new framework and methods to lower bound DTWCode0
Elastic Product Quantization for Time SeriesCode0
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link PredictionCode0
End-to-end learning of energy-based representations for irregularly-sampled signals and imagesCode0
Efficient Matrix Profile Computation Using Different Distance FunctionsCode0
Deep Imbalanced Time-series Forecasting via Local Discrepancy DensityCode0
A New Valuation Measure for the Stock MarketCode0
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through timeCode0
Efficient learning of nonlinear prediction models with time-series privileged informationCode0
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous VariablesCode0
Deep Efficient Continuous Manifold Learning for Time Series ModelingCode0
Automated Deep Abstractions for Stochastic Chemical Reaction NetworksCode0
Efficient Covariance Estimation from Temporal DataCode0
Edge computing on TPU for brain implant signal analysisCode0
Soft and subspace robust multivariate rank tests based on entropy regularized optimal transportCode0
EasyMLServe: Easy Deployment of REST Machine Learning ServicesCode0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
Economy Statistical Recurrent Units For Inferring Nonlinear Granger CausalityCode0
Agglomerative Likelihood ClusteringCode0
Early Abandoning PrunedDTW and its application to similarity searchCode0
Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine LearningCode0
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
Dynamic transformation of prior knowledge into Bayesian models for data streamsCode0
Dynamic Natural Language Processing with Recurrence Quantification AnalysisCode0
Approximate Factor Models for Functional Time SeriesCode0
Dynamic process fault prediction using canonical variable trend analysisCode0
Dynamic Virtual Graph Significance Networks for Predicting InfluenzaCode0
Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor StreamsCode0
DynaConF: Dynamic Forecasting of Non-Stationary Time SeriesCode0
Dynamic cyber risk estimation with Competitive Quantile AutoregressionCode0
Spatiotemporal Attention Networks for Wind Power ForecastingCode0
Anomaly Detection with Generative Adversarial Networks for Multivariate Time SeriesCode0
DTW-Merge: A Novel Data Augmentation Technique for Time Series ClassificationCode0
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one MapsCode0
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence ModelCode0
DSSRNN: Decomposition-Enhanced State-Space Recurrent Neural Network for Time-Series AnalysisCode0
DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural NetworksCode0
Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific FeaturesCode0
Deep Learning for Predicting Asset ReturnsCode0
Predicting Sparse Clients' Actions with CPOPT-Net in the Banking EnvironmentCode0
Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge SolutionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified