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 2126–2150 of 6748 papers

TitleStatusHype
Detecting and explaining changes in various assets' relationships in financial markets—0
Basic Filters for Convolutional Neural Networks Applied to Music: Training or Design?—0
DETECT: A Hierarchical Clustering Algorithm for Behavioural Trends in Temporal Educational Data—0
Bandwidth-efficient distributed neural network architectures with application to body sensor networks—0
Animal Behavior Classification via Accelerometry Data and Recurrent Neural Networks—0
A Fast Evidential Approach for Stock Forecasting—0
Deteção de estruturas permanentes a partir de dados de séries temporais Sentinel 1 e 2—0
Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data—0
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition—0
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data—0
Design-time Fashion Popularity Forecasting in VR Environments—0
Bag of Recurrence Patterns Representation for Time-Series Classification—0
Characterizing the memory capacity of transmon qubit reservoirs—0
Deriving land surface phenology indicators from CO2 eddy covariance measurements—0
ANFIS-based prediction of power generation for combined cycle power plant—0
Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks—0
Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals—0
Backpropagation on Dynamical Networks—0
Dependent Matérn Processes for Multivariate Time Series—0
Backpropagation-Free Learning Method for Correlated Fuzzy Neural Networks—0
Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective—0
Dense Bag-of-Temporal-SIFT-Words for Time Series Classification—0
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks—0
Denoising neural networks for magnetic resonance spectroscopy—0
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models—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