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 451–500 of 6748 papers

TitleStatusHype
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulationsCode1
Evaluation of post-hoc interpretability methods in time-series classificationCode1
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series AnalysisCode1
CKConv: Continuous Kernel Convolution For Sequential DataCode1
A Neural PDE Solver with Temporal Stencil ModelingCode1
Adaptive Conformal Predictions for Time SeriesCode1
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
ClaSP -- Parameter-free Time Series SegmentationCode1
Last Query Transformer RNN for knowledge tracingCode1
Learning Fast and Slow for Online Time Series ForecastingCode1
Learning from Irregularly-Sampled Time Series: A Missing Data PerspectiveCode1
Closed-Form Diffeomorphic Transformations for Time Series AlignmentCode1
Learning Graph Neural Networks for Multivariate Time Series Anomaly DetectionCode1
Adversarial Sparse Transformer for Time Series ForecastingCode1
Learning Long-Term Dependencies in Irregularly-Sampled Time SeriesCode1
A Comprehensive Survey of Regression Based Loss Functions for Time Series ForecastingCode1
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural NetworksCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse ObservationsCode1
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural NetworksCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
Changing Fashion CulturesCode1
An Experimental Review on Deep Learning Architectures for Time Series ForecastingCode1
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognitionCode1
Long Expressive Memory for Sequence ModelingCode1
Long Short-Term Memory Spiking Networks and Their ApplicationsCode1
Long-term series forecasting with Query Selector -- efficient model of sparse attentionCode1
LSTM-Autoencoder based Anomaly Detection for Indoor Air Quality Time Series DataCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
Can LLMs Understand Time Series Anomalies?Code1
Machine Learning Time Series Regressions with an Application to NowcastingCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
MALI: A memory efficient and reverse accurate integrator for Neural ODEsCode1
Affect2MM: Affective Analysis of Multimedia Content Using Emotion CausalityCode1
Manifold Topology Divergence: a Framework for Comparing Data Manifolds.Code1
Market regime classification with signaturesCode1
Massively Parallel Causal Inference of Whole Brain Dynamics at Single Neuron ResolutionCode1
A biologically plausible neural network for Slow Feature AnalysisCode1
Memory-free Online Change-point Detection: A Novel Neural Network ApproachCode1
Meta-learning framework with applications to zero-shot time-series forecastingCode1
Methodology for forecasting and optimization in IEEE-CIS 3rd Technical ChallengeCode1
Calibration of Google Trends Time SeriesCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
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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