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 151–175 of 6748 papers

TitleStatusHype
Event Detection via Probability Density Function RegressionCode0
Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis—0
Topological Representational Similarity Analysis in Brains and Beyond—0
Agentic Retrieval-Augmented Generation for Time Series Analysis—0
Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Method—0
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease AssessmentCode1
Time Series Analysis by State Space Learning—0
MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series AnalysisCode2
Unleashing The Power of Pre-Trained Language Models for Irregularly Sampled Time Series—0
Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach—0
An End-to-End Model for Time Series Classification In the Presence of Missing Values—0
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection—0
Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series AnalysisCode1
Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 PandemicCode0
Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction—0
Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis—0
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series—0
HIERVAR: A Hierarchical Feature Selection Method for Time Series Analysis—0
Omni-Dimensional Frequency Learner for General Time Series Analysis—0
Parameter inference from a non-stationary unknown processCode0
Guidelines for Augmentation Selection in Contrastive Learning for Time Series ClassificationCode0
ViTime: A Visual Intelligence-Based Foundation Model for Time Series ForecastingCode2
A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power System—0
Prediction of Sentinel-2 multi-band imagery with attention BiLSTM for continuous earth surface monitoring—0
Filtration learning in exact multi-parameter persistent homology and classification of time-series data—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