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 1651–1700 of 6748 papers

TitleStatusHype
Anamnesic Neural Differential Equations with Orthogonal Polynomial ProjectionsCode0
Probability Paths and the Structure of Predictions over TimeCode0
Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-SeriesCode0
Accelerating Neural Architecture Search using Performance PredictionCode0
Enhancing Identification of Structure Function of Academic Articles Using Contextual InformationCode0
Enhancing Time Series Momentum Strategies Using Deep Neural NetworksCode0
End-to-End Learned Early Classification of Time Series for In-Season Crop Type MappingCode0
End-to-end learning of energy-based representations for irregularly-sampled signals and imagesCode0
Causal Discovery with Attention-Based Convolutional Neural NetworksCode0
Causal Discovery using Model Invariance through Knockoff InterventionsCode0
Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time SeriesCode0
Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural NetworksCode0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State NetworkCode0
Dataset: Rare Event Classification in Multivariate Time SeriesCode0
Causal discovery for time series with latent confoundersCode0
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link PredictionCode0
Raising the ClaSS of Streaming Time Series SegmentationCode0
Probabilistic Traffic Forecasting with Dynamic RegressionCode0
Ensembles of Randomized Time Series Shapelets Provide Improved Accuracy while Reducing Computational CostsCode0
Evaluating time series forecasting models: An empirical study on performance estimation methodsCode0
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 MethodsCode0
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series DataCode0
Efficient learning of nonlinear prediction models with time-series privileged informationCode0
DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time SeriesCode0
Real-time Power System State Estimation and Forecasting via Deep Neural NetworksCode0
Efficient Matrix Profile Computation Using Different Distance FunctionsCode0
Real Time Trajectory Prediction Using Deep Conditional Generative ModelsCode0
Attaining entropy production and dissipation maps from Brownian movies via neural networksCode0
RecovDB: accurate and efficient missing blocks recovery for large time seriesCode0
Time-Series Event Prediction with Evolutionary State GraphCode0
Quantum open system identification via global optimization: Optimally accurate Markovian models of open systems from time-series dataCode0
Efficient Certified Training and Robustness Verification of Neural ODEsCode0
Deep Efficient Continuous Manifold Learning for Time Series ModelingCode0
Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal AnalysisCode0
Efficient Covariance Estimation from Temporal DataCode0
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous VariablesCode0
Caulking the Leakage Effect in MEEG Source Connectivity AnalysisCode0
EasyMLServe: Easy Deployment of REST Machine Learning ServicesCode0
Economy Statistical Recurrent Units For Inferring Nonlinear Granger CausalityCode0
Early Abandoning PrunedDTW and its application to similarity searchCode0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
An attention model to analyse the risk of agitation and urinary tract infections in people with dementiaCode0
Edge computing on TPU for brain implant signal analysisCode0
Dynamic transformation of prior knowledge into Bayesian models for data streamsCode0
Dynamic Virtual Graph Significance Networks for Predicting InfluenzaCode0
A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groupsCode0
MASA: Motif-Aware State Assignment in Noisy Time Series DataCode0
Reinforcement Learning for Portfolio ManagementCode0
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
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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