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 5476–5500 of 6748 papers

TitleStatusHype
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series—0
Deep Bayesian Nonparametric Tracking—0
Robust and Scalable Models of Microbiome Dynamics—0
Stock Movement Prediction from Tweets and Historical PricesCode0
Accurate Uncertainties for Deep Learning Using Calibrated RegressionCode0
Sampling and Reconstruction of Signals on Product GraphsCode0
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference—0
Nonlinearity in stock networks—0
Multilevel Wavelet Decomposition Network for Interpretable Time Series AnalysisCode0
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention—0
What Makes An Asset Useful?—0
Complex Gated Recurrent Neural NetworksCode0
A Review of Network Inference Techniques for Neural Activation Time SeriesCode0
Kernel Methods for Nonlinear Connectivity Detection—0
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks—0
Multi-variable LSTM neural network for autoregressive exogenous model—0
Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-assisted Surgery—0
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
A review on distance based time series classification—0
The Role of Agricultural Sector Productivity in Economic Growth: The Case of Iran's Economic Development Plan—0
Stationary Geometric Graphical Model Selection—0
Discovering Signals from Web Sources to Predict Cyber Attacks—0
SOM-VAE: Interpretable Discrete Representation Learning on Time SeriesCode0
EigenNetworks—0
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalsCode1
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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