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 5051–5075 of 6748 papers

TitleStatusHype
Convolution, attention and structure embedding—0
On Transfer Learning For Chatter Detection in Turning Using Wavelet Packet Transform and Empirical Mode DecompositionCode0
Temporal Graph Convolutional Networks for Automatic Seizure Detection—0
Pyramid Recurrent Neural Networks for Multi-Scale Change-Point Detection—0
Boosting: Why You Can Use the HP FilterCode0
Surface Type Classification for Autonomous Robot Indoor Navigation—0
The Expressive Power of Gated Recurrent Units as a Continuous Dynamical System—0
A fully automated periodicity detection in time seriesCode0
A Novel Trend Symbolic Aggregate Approximation for Time Series—0
Context-Dependent Semantic Parsing over Temporally Structured DataCode0
Neural Causal Discovery with Learnable Input Noise—0
GEOMETRIC AUGMENTATION FOR ROBUST NEURAL NETWORK CLASSIFIERS—0
Time-series Insights into the Process of Passing or Failing Online University Courses using Neural-Induced Interpretable Student States—0
Learning Procedural Abstractions and Evaluating Discrete Latent Temporal StructureCode0
Sequence to sequence deep learning models for solar irradiation forecasting—0
Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT)—0
Curriculum Learning in Deep Neural Networks for Financial Forecasting—0
Empirical facts characterizing banking crises: an analysis via binary time series—0
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification—0
Rough volatility of Bitcoin—0
Cough Detection Using Hidden Markov Models—0
Real numbers, data science and chaos: How to fit any dataset with a single parameterCode0
Temporal-Clustering Invariance in Irregular Healthcare Time Series—0
Discovering Common Change-Point Patterns in Functional Connectivity Across Subjects—0
Time Series Simulation by Conditional Generative Adversarial Net—0
Show:102550
← PrevPage 203 of 270Next →
All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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