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 3051–3075 of 6748 papers

TitleStatusHype
Hierarchical Linear Dynamical System for Representing Notes from Recorded Audio—0
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data—0
Hierarchically Regularized Deep Forecasting—0
Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification—0
Hierarchical Multi-resolution Mesh Networks for Brain Decoding—0
A statistical test of market efficiency based on information theory—0
Hierarchical Quickest Change Detection via Surrogates—0
Hierarchical regularization networks for sparsification based learning on noisy datasets—0
Causal Graph Discovery from Self and Mutually Exciting Time Series—0
Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data—0
Hierarchical Time Series Forecasting with Bayesian Modeling—0
High Dimensional Forecasting via Interpretable Vector Autoregression—0
A Novel Framework for Handling Sparse Data in Traffic Forecast—0
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
IIT-GAN: Irregular and Intermittent Time-series Synthesis with Generative Adversarial Networks—0
HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions—0
Correlations and Flow of Information between The New York Times and Stock Markets—0
High dimensional Bayesian Optimization Algorithm for Complex System in Time Series—0
High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series—0
High-Dimensional Granger Causality for Climatic Attribution—0
Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification—0
End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding—0
Cost-Effective Bad Synchrophasor Data Detection Based on Unsupervised Time Series Data Analytics—0
High-dimensional Multivariate Time Series Forecasting in IoT Applications using Embedding Non-stationary Fuzzy Time Series—0
A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic Programming—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