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 2251–2275 of 6748 papers

TitleStatusHype
A new hazard event classification model via deep learning and multifractal—0
Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network—0
Application of Deep Interpolation Network for Clustering of Physiologic Time Series—0
Patient-Specific Effects of Medication Using Latent Force Models with Gaussian Processes—0
Classification of chaotic time series with deep learning—0
Application of Common Spatial Patterns in Gravitational Waves Detection—0
Classification Models for Partially Ordered Sequences—0
Classification des Séries Temporelles Incertaines par Transformation Shapelet—0
A Lane-Changing Prediction Method Based on Temporal Convolution Network—0
Estimation of Correlation Matrices from Limited time series Data using Machine Learning—0
Estimation of multivariate asymmetric power GARCH models—0
Ethereum Price Prediction Employing Large Language Models for Short-term and Few-shot Forecasting—0
Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal Data Fusion and Feature Extraction—0
A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting—0
CLARE-GAN: GENERATION OF CLASS-SPECIFIC TIME SERIES—0
A posteriori Trading-inspired Model-free Time Series Segmentation—0
Estimating value at risk: LSTM vs. GARCH—0
A posteriori multi-stage optimal trading under transaction costs and a diversification constraint—0
Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction—0
Circulant Singular Spectrum Analysis: A new automated procedure for signal extraction—0
Cine-MRI detection of abdominal adhesions with spatio-temporal deep learning—0
A point process approach for the classification of noisy calcium imaging data—0
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders—0
Churn prediction in online gambling—0
ChunkFormer: Learning Long Time Series with Multi-stage Chunked Transformer—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