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

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Showing 201–225 of 6748 papers

TitleStatusHype
Spatial, Temporal, and Geometric Fusion for Remote Sensing Images—0
Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks—0
Evaluating Large Language Models on Time Series Feature Understanding: A Comprehensive Taxonomy and Benchmark—0
Review of Data-centric Time Series Analysis from Sample, Feature, and Period—0
Mamba-360: Survey of State Space Models as Transformer Alternative for Long Sequence Modelling: Methods, Applications, and ChallengesCode2
TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis—0
Deep Learning for Satellite Image Time Series Analysis: A Review—0
Log-PDE Methods for Rough Signature Kernels—0
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide—0
Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series AnalysisCode1
A Survey on State-of-the-art Deep Learning Applications and Challenges—0
A data-informed mathematical model of microglial cell dynamics during ischemic stroke in the middle cerebral artery—0
Foundation Models for Time Series Analysis: A Tutorial and SurveyCode7
Capsule Neural Networks as Noise Stabilizer for Time Series Data—0
Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Models—0
Advancing multivariate time series similarity assessment: an integrated computational approach—0
Self-Supervised Learning for Time Series: Contrastive or Generative?Code1
Caformer: Rethinking Time Series Analysis from Causal Perspective—0
Time Series Analysis of Key Societal Events as Reflected in Complex Social Media Data Streams—0
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time SeriesCode0
ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis—0
Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance—0
Time Series Analysis in Compressor-Based Machines: A Survey—0
TOTEM: TOkenized Time Series EMbeddings for General Time Series AnalysisCode3
IMUOptimize: A Data-Driven Approach to Optimal IMU Placement for Human Pose Estimation with Transformer Architecture—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