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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1951–1975 of 6748 papers

TitleStatusHype
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class ClassificationCode0
Dilated Convolutional Neural Networks for Time Series ForecastingCode0
Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time SeriesCode0
Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?Code0
A Framework for Imbalanced Time-series ForecastingCode0
Bidirectional deep-readout echo state networksCode0
Diffeomorphic Temporal Alignment NetsCode0
Beyond Sparsity: Tree Regularization of Deep Models for InterpretabilityCode0
Unsupervised Representation Learning of Structured Radio Communication SignalsCode0
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly DetectionCode0
Detrended Partial-Cross-Correlation Analysis: A New Method for Analyzing Correlations in Complex SystemCode0
Discovering Synchronized Subsets of Sequences: A Large Scale SolutionCode0
Using Clinical Notes with Time Series Data for ICU ManagementCode0
Using generalized additive models to decompose time series and waveforms, and dissect heart-lung interaction physiologyCode0
DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signalCode0
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEsCode0
Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural NetworksCode0
On projection methods for functional time series forecastingCode0
Beyond Predictions in Neural ODEs: Identification and Interventions—0
Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data—0
Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning—0
Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion—0
Better than DFA? A Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences—0
Anomalous volatility scaling in high frequency financial data—0
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—0
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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