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 3001–3025 of 6748 papers

TitleStatusHype
Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial FibrillationCode0
Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks—0
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial AttackCode1
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps—0
Generating Reliable Process Event Streams and Time Series Data based on Neural NetworksCode0
UnICORNN: A recurrent model for learning very long time dependenciesCode1
Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks—0
Classification and Feature Transformation with Fuzzy Cognitive MapsCode0
PyRCN: A Toolbox for Exploration and Application of Reservoir Computing NetworksCode1
Optimizing Expected Shortfall under an _1 constraint -- an analytic approach—0
Artificial neural network as a universal model of nonlinear dynamical systems—0
LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management—0
Testing for a Random Walk Structure in the Frequency Evolution of a Tone in Noise—0
On the LRD of the Aggregated Traffic Flows in High-Speed Computer Networks—0
Deep Hedging, Generative Adversarial Networks, and Beyond—0
Prediction of financial time series using LSTM and data denoising methods—0
District Wise Price Forecasting of Wheat in Pakistan using Deep Learning—0
Theory of Evolutionary Spectra for Heteroskedasticity and Autocorrelation Robust Inference in Possibly Misspecified and Nonstationary Models—0
Exploring the representativeness of the M5 competition data—0
Modeling Macroeconomic Variations After COVID-19—0
LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment—0
Contrastive learning of strong-mixing continuous-time stochastic processes—0
Two-Stage Framework for Seasonal Time Series Forecasting—0
Scalable Learning With a Structural Recurrent Neural Network for Short-Term Traffic PredictionCode1
Video Sentiment Analysis with Bimodal Information-augmented Multi-Head Attention—0
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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