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 1076–1100 of 6748 papers

TitleStatusHype
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
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
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
IMUOptimize: A Data-Driven Approach to Optimal IMU Placement for Human Pose Estimation with Transformer Architecture—0
Incorporating Taylor Series and Recursive Structure in Neural Networks for Time Series Prediction—0
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting—0
Multi-scale fMRI time series analysis for understanding neurodegeneration in MCI—0
Assessing the Impact of Distribution Shift on Reinforcement Learning Performance—0
Empowering Time Series Analysis with Large Language Models: A Survey—0
Efficient Market Dynamics: Unraveling Informational Efficiency in UK Horse Racing Betting Markets Through Betfair's Time Series Analysis—0
Conditioning non-linear and infinite-dimensional diffusion processesCode0
Distillation Enhanced Time Series Forecasting Network with Momentum Contrastive LearningCode0
Efficient Observation Time Window Segmentation for Administrative Data Machine Learning—0
TNANet: A Temporal-Noise-Aware Neural Network for Suicidal Ideation Prediction with Noisy Physiological Data—0
Parsimony or Capability? Decomposition Delivers Both in Long-term Time Series Forecasting—0
Machine learning approach to detect dynamical states from recurrence measures—0
Complex systems approach to natural language—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