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 1126–1150 of 6748 papers

TitleStatusHype
Hidden Parameter Recurrent State Space Models For Changing Dynamics ScenariosCode0
Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance MatricesCode0
Hide-and-Seek Privacy ChallengeCode0
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk PredictionCode0
Hierarchical Attention-Based Recurrent Highway Networks for Time Series PredictionCode0
High-dimensional regression with potential prior information on variable importanceCode0
Harnessing the power of Topological Data Analysis to detect change points in time seriesCode0
Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 PandemicCode0
Guidelines for Augmentation Selection in Contrastive Learning for Time Series ClassificationCode0
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal frameworkCode0
Analysis of Thai Capital Market Linkages: Part I. Bivariate Copula ApproachCode0
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series DataCode0
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge AggregationCode0
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time seriesCode0
ATCN: Resource-Efficient Processing of Time Series on EdgeCode0
Graph Gamma Process Generalized Linear Dynamical SystemsCode0
Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural NetworkCode0
Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel DataCode0
Graph Edit NetworksCode0
Gradient-free training of recurrent neural networksCode0
Granger Causality using Neural NetworksCode0
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series DataCode0
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsCode0
Hybrid Deep Neural Networks to Infer State Models of Black-Box SystemsCode0
Generative Optimization Networks for Memory Efficient Data GenerationCode0
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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