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 1951–1975 of 6748 papers

TitleStatusHype
On the Parameterization and Initialization of Diagonal State Space Models—0
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements—0
A Transfer-Learning Based Ensemble Architecture for ECG Signal Classification—0
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces—0
Twitter conversations predict the daily confirmed COVID-19 casesCode0
Statistical inference of lead-lag at various timescales between asynchronous time series from p-values of transfer entropy—0
An Agent-Based Model With Realistic Financial Time Series: A Method for Agent-Based Models Validation—0
DisCoVQA: Temporal Distortion-Content Transformers for Video Quality AssessmentCode0
Understanding the Role of Weather Data for Earth Surface Forecasting using a ConvLSTM-based ModelCode0
Time Gated Convolutional Neural Networks for Crop Classification—0
Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting—0
Autoencoding Conditional GAN for Portfolio Allocation Diversification—0
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift—0
Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings—0
Cyclocopula Technique to Study the Relationship Between Two Cyclostationary Time Series with Fractional Brownian Motion Errors—0
User Engagement in Mobile Health Applications—0
Domain Generalization via Selective Consistency Regularization for Time Series Classification—0
Learning with little mixing—0
Evaluating Short-Term Forecasting of Multiple Time Series in IoT EnvironmentsCode0
Calibrating Agent-based Models to Microdata with Graph Neural Networks—0
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning—0
Improving Solar Flare Prediction by Time Series Outlier Detection—0
Robust Time Series Denoising with Learnable Wavelet Packet Transform—0
Safe-FinRL: A Low Bias and Variance Deep Reinforcement Learning Implementation for High-Freq Stock Trading—0
Using generalized additive models to decompose time series and waveforms, and dissect heart-lung interaction physiologyCode0
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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