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 2026–2050 of 6748 papers

TitleStatusHype
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets—0
Discovering Hidden Physics Behind Transport Dynamics—0
Discovering Invariances in Healthcare Neural Networks—0
An LLM-Powered Agent for Physiological Data Analysis: A Case Study on PPG-based Heart Rate Estimation—0
Comparing Time-Series Analysis Approaches Utilized in Research Papers to Forecast COVID-19 Cases in Africa: A Literature Review—0
Comparing the forecasting of cryptocurrencies by Bayesian time-varying volatility models—0
Discovering ordinary differential equations that govern time-series—0
Benefit-aware Early Prediction of Health Outcomes on Multivariate EEG Time Series—0
Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game Data—0
Discovering Potential Correlations via Hypercontractivity—0
Discovering Latent Covariance Structures for Multiple Time Series—0
Discovering Signals from Web Sources to Predict Cyber Attacks—0
Benign Overfitting in Time Series Linear Models with Over-Parameterization—0
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends—0
Discovering Volatile Events in Your Neighborhood: Local-Area Topic Extraction from Blog Entries—0
Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm—0
Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion—0
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation—0
Discrete MDL Predicts in Total Variation—0
Beyond Predictions in Neural ODEs: Identification and Interventions—0
Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters—0
Dynamic Probabilistic Network Based Human Action Recognition—0
Comparing statistical and machine learning methods for time series forecasting in data-driven logistics -- A simulation study—0
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images—0
Comparing seven methods for state-of-health time series prediction for the lithium-ion battery packs of forklifts—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