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 651–675 of 6748 papers

TitleStatusHype
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series DataCode1
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological OrderCode1
Forecasting in Non-stationary Environments with Fuzzy Time SeriesCode1
Hierarchical forecasting with a top-down alignment of independent level forecastsCode1
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series ForecastingCode1
A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingCode1
From Fourier to Koopman: Spectral Methods for Long-term Time Series PredictionCode1
From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecastingCode1
Fully Spiking Variational AutoencoderCode1
FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural NetworksCode1
Gated Transformer Networks for Multivariate Time Series ClassificationCode1
Gaussian Process Prior Variational AutoencodersCode1
Calibration of Google Trends Time SeriesCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval PredictorsCode1
Global RTK Positioning in Graphical State SpaceCode1
Arbitrage-free neural-SDE market modelsCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
Graph Neural Network-Based Anomaly Detection in Multivariate Time SeriesCode1
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Bilinear Input Normalization for Neural Networks in Financial ForecastingCode1
Active multi-fidelity Bayesian online changepoint detectionCode1
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading RulesCode1
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