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 1701–1750 of 6748 papers

TitleStatusHype
Enhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized ModelsCode0
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman FilterCode0
Ensemble transport smoothing. Part I: Unified frameworkCode0
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time SeriesCode0
Explaining Deep Classification of Time-Series Data with Learned PrototypesCode0
Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 PandemicCode0
End-to-end learning of energy-based representations for irregularly-sampled signals and imagesCode0
Probabilistic Traffic Forecasting with Dynamic RegressionCode0
Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time SeriesCode0
End-to-End Learned Early Classification of Time Series for In-Season Crop Type MappingCode0
Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-SeriesCode0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural NetworksCode0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
Elastic Product Quantization for Time SeriesCode0
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link PredictionCode0
Enhancing Identification of Structure Function of Academic Articles Using Contextual InformationCode0
Caulking the Leakage Effect in MEEG Source Connectivity AnalysisCode0
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous VariablesCode0
Efficient learning of nonlinear prediction models with time-series privileged informationCode0
The Neural Moving Average Model for Scalable Variational Inference of State Space ModelsCode0
Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devicesCode0
Efficient Matrix Profile Computation Using Different Distance FunctionsCode0
Augmenting Neural Differential Equations to Model Unknown Dynamical Systems with Incomplete State InformationCode0
A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groupsCode0
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series PredictionCode0
MASA: Motif-Aware State Assignment in Noisy Time Series DataCode0
Efficient Covariance Estimation from Temporal DataCode0
Elastic bands across the path: A new framework and methods to lower bound DTWCode0
Economy Statistical Recurrent Units For Inferring Nonlinear Granger CausalityCode0
Capturing the temporal constraints of gradual patternsCode0
Edge computing on TPU for brain implant signal analysisCode0
Capturing Structure Implicitly from Time-Series having Limited DataCode0
Capturing Actionable Dynamics with Structured Latent Ordinary Differential EquationsCode0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
Early Abandoning PrunedDTW and its application to similarity searchCode0
EasyMLServe: Easy Deployment of REST Machine Learning ServicesCode0
Efficient Certified Training and Robustness Verification of Neural ODEsCode0
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
A New Valuation Measure for the Stock MarketCode0
Dynamic transformation of prior knowledge into Bayesian models for data streamsCode0
Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine LearningCode0
Dynamic Virtual Graph Significance Networks for Predicting InfluenzaCode0
Dynamic process fault prediction using canonical variable trend analysisCode0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial FibrillationCode0
Dynamic Natural Language Processing with Recurrence Quantification AnalysisCode0
Deep Efficient Continuous Manifold Learning for Time Series ModelingCode0
Dynamic cyber risk estimation with Competitive Quantile AutoregressionCode0
DynaConF: Dynamic Forecasting of Non-Stationary Time SeriesCode0
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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