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 826–850 of 6748 papers

TitleStatusHype
Root Cause Detection Among Anomalous Time Series Using Temporal State AlignmentCode1
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Can LLMs Understand Time Series Anomalies?Code1
Scalable Learning With a Structural Recurrent Neural Network for Short-Term Traffic PredictionCode1
Scalable Spatiotemporal Graph Neural NetworksCode1
Bilinear Input Normalization for Neural Networks in Financial ForecastingCode1
Second-Order Neural ODE OptimizerCode1
Selecting time-series hyperparameters with the artificial jackknifeCode1
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
Benchmark time series data sets for PyTorch -- the torchtime packageCode1
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
Self-Supervised Time Series Representation Learning by Inter-Intra Relational ReasoningCode1
AtsPy: Automated Time Series Forecasting in PythonCode1
SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud RemovalCode1
Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series ClassificationCode1
Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical ModelCode1
SepTr: Separable Transformer for Audio Spectrogram ProcessingCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series ClassificationCode1
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
Signal Decomposition Using Masked Proximal OperatorsCode1
Similarity Learning based Few Shot Learning for ECG Time Series ClassificationCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
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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