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 451–475 of 6748 papers

TitleStatusHype
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulationsCode1
Cost-effective Interactive Attention Learning with Neural Attention ProcessesCode1
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local ExplanationsCode1
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal BootstrappingCode1
COT-GAN: Generating Sequential Data via Causal Optimal TransportCode1
A Neural PDE Solver with Temporal Stencil ModelingCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time SeriesCode1
An Evaluation of Change Point Detection AlgorithmsCode1
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series DataCode1
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural NetworksCode1
Discovering Nonlinear Relations with Minimum Predictive Information RegularizationCode1
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact DataCode1
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time SeriesCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential EquationsCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
Domain Adaptation for Time Series Forecasting via Attention SharingCode1
Discrete Graph Structure Learning for Forecasting Multiple Time SeriesCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
CUTS: Neural Causal Discovery from Irregular Time-Series DataCode1
Are we certain it's anomalous?Code1
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognitionCode1
Diffusion Generative Models in Infinite DimensionsCode1
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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