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 601–625 of 6748 papers

TitleStatusHype
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series dataCode1
Monte Carlo Simulation of SDEs using GANsCode1
Stock price prediction using Generative Adversarial NetworksCode1
Neural Transformation Learning for Deep Anomaly Detection Beyond ImagesCode1
Stiff Neural Ordinary Differential EquationsCode1
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
Evaluation of deep learning models for multi-step ahead time series predictionCode1
Gated Transformer Networks for Multivariate Time Series ClassificationCode1
Active multi-fidelity Bayesian online changepoint detectionCode1
Neural ODE ProcessesCode1
An Experimental Review on Deep Learning Architectures for Time Series ForecastingCode1
An Empirical Framework for Domain Generalization in Clinical SettingsCode1
Learning Discriminative Prototypes with Dynamic Time WarpingCode1
Temporal Cluster Matching for Change Detection of Structures from Satellite ImageryCode1
Hierarchical forecasting with a top-down alignment of independent level forecastsCode1
Spectral Temporal Graph Neural Network for Multivariate Time-series ForecastingCode1
Affect2MM: Affective Analysis of Multimedia Content Using Emotion CausalityCode1
UnICORNN: A recurrent model for learning very long time dependenciesCode1
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial AttackCode1
PyRCN: A Toolbox for Exploration and Application of Reservoir Computing NetworksCode1
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time SeriesCode1
Scalable Learning With a Structural Recurrent Neural Network for Short-Term Traffic PredictionCode1
Missing Value Imputation on Multidimensional Time SeriesCode1
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time SeriesCode1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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