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 1051–1075 of 6748 papers

TitleStatusHype
Understanding Different Design Choices in Training Large Time Series Models—0
Energy-Efficient Seizure Detection Suitable for low-power Applications—0
Probabilistic Deep Learning and Transfer Learning for Robust Cryptocurrency Price PredictionCode0
Game of LLMs: Discovering Structural Constructs in Activities using Large Language Models—0
Data Augmentation for Multivariate Time Series Classification: An Experimental Study—0
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation—0
Efficient Time Series Processing for Transformers and State-Space Models through Token Merging—0
UnitNorm: Rethinking Normalization for Transformers in Time Series—0
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting—0
An Active Learning Framework with a Class Balancing Strategy for Time Series Classification—0
UniCL: A Universal Contrastive Learning Framework for Large Time Series Models—0
AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis—0
WEITS: A Wavelet-enhanced residual framework for interpretable time series forecasting—0
Kolmogorov-Arnold Networks (KANs) for Time Series Analysis—0
TS3IM: Unveiling Structural Similarity in Time Series through Image Similarity Assessment Insights—0
From Generalization Analysis to Optimization Designs for State Space Models—0
Quantitative Tools for Time Series Analysis in Natural Language Processing: A Practitioners GuideCode0
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
TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis—0
Deep Learning for Satellite Image Time Series Analysis: A Review—0
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide—0
Log-PDE Methods for Rough Signature Kernels—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