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 4001–4025 of 6748 papers

TitleStatusHype
Trading styles and long-run variance of asset prices—0
Traffic Flow Combination Forecasting Method Based on Improved LSTM and ARIMA—0
Traffic Flows Analysis in High-Speed Computer Networks Using Time Series—0
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting—0
Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting—0
Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition—0
Training Algorithm for Neuro-Fuzzy Network Based on Singular Spectrum Analysis—0
Training and Analysing Deep Recurrent Neural Networks—0
Training Deep Fourier Neural Networks To Fit Time-Series Data—0
Training Distributed Deep Recurrent Neural Networks with Mixed Precision on GPU Clusters—0
Training Dynamic Exponential Family Models with Causal and Lateral Dependencies for Generalized Neuromorphic Computing—0
Training Echo State Networks with Regularization through Dimensionality Reduction—0
Training-free LLM-generated Text Detection by Mining Token Probability Sequences—0
Training Linear Finite-State Machines—0
"Train one, Classify one, Teach one" -- Cross-surgery transfer learning for surgical step recognition—0
TRAKR – A reservoir-based tool for fast and accurate classification of neural time-series patterns—0
Transfer Entropy: where Shannon meets Turing—0
Transfer Learning-based State of Health Estimation for Lithium-ion Battery with Cycle Synchronization—0
Transfer Learning for Autonomous Chatter Detection in Machining—0
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks—0
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks—0
Transfer learning for cross-modal demand prediction of bike-share and public transit—0
Transfer learning of chaotic systems—0
Transform-Based Multilinear Dynamical System for Tensor Time Series Analysis—0
Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets—0
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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