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 2501–2525 of 6748 papers

TitleStatusHype
Explainable classification of astronomical uncertain time series—0
Ensemble of Hankel Matrices for Face Emotion Recognition—0
Explainable Failure Predictions with RNN Classifiers based on Time Series Data—0
Explainable Framework for Time-series Analysis via Topological Data Analysis—0
Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop—0
EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid—0
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA—0
Q4EDA: A Novel Strategy for Textual Information Retrieval Based on User Interactions with Visual Representations of Time Series—0
Explainable Recommendation: Theory and Applications—0
Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting—0
Few-shot Learning for Time-series Forecasting—0
Ensemble neuroevolution based approach for multivariate time series anomaly detection—0
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction—0
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning—0
Explaining Outcomes of Multi-Party Dialogues using Causal Learning—0
Explaining Time Series by Counterfactuals—0
Causal Structural Learning from Time Series: A Convex Optimization Approach—0
Explicit Domain Adaptation with Loosely Coupled Samples—0
Explicit-Duration Markov Switching Models—0
Explicitly Solvable Continuous-time Inference for Partially Observed Markov Processes—0
Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning—0
Exploiting Social Relations and Sentiment for Stock Prediction—0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
Exploiting statistical dependencies of time series with hierarchical correlation reconstruction—0
Ensemble Grammar Induction For Detecting Anomalies in Time Series—0
Show:102550
← PrevPage 101 of 270Next →
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