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 1901–1925 of 6748 papers

TitleStatusHype
Inferential Theory for Granular Instrumental Variables in High Dimensions—0
Improving the quality control of seismic data through active learning—0
SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window TransformersCode1
Predicting waves in fluids with deep neural network—0
Continual Transformers: Redundancy-Free Attention for Online InferenceCode1
Forecasting COVID-19 Caseloads Using Unsupervised Embedding Clusters of Social Media Posts—0
Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters—0
Enhancement of Healthcare Data Performance Metrics using Neural Network Machine Learning Algorithms—0
Fractional SDE-Net: Generation of Time Series Data with Long-term Memory—0
Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique—0
Time Series Generation with Masked AutoencoderCode1
IDEA: Interpretable Dynamic Ensemble Architecture for Time Series Prediction—0
An efficient aggregation method for the symbolic representation of temporal dataCode1
Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction—0
A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian MutationCode0
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series ForecastingCode1
Privacy Amplification by Subsampling in Time Domain—0
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection—0
Recursive Least Squares Policy Control with Echo State Network—0
Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial NetworksCode0
Time-varying Identification of Guided Wave Propagation under Varying Temperature via Non-Stationary Time Series Models—0
Generative time series models using Neural ODE in Variational AutoencodersCode1
Cognitive forces shape the dynamics of word usage across multiple languages—0
Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?—0
Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity?—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