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

Showing 54515500 of 6748 papers

TitleStatusHype
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems0
Scene Learning: Deep Convolutional Networks For Wind Power Prediction by Embedding Turbines into Grid Space0
Time Series Deinterleaving of DNS Traffic0
Assessment of electrical and infrastructure recovery in Puerto Rico following hurricane Maria using a multisource time series of satellite imagery0
Analysis of Advisor Portfolio using Multivariate Time Series and Cosine Similarity0
Tracking the Evolution of Words with Time-reflective Text Representations0
Inferring Multidimensional Rates of Aging from Cross-Sectional DataCode0
SVD-based Visualisation and Approximation for Time Series Data in Smart Energy Systems0
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process0
Adaptive Learning Method of Recurrent Temporal Deep Belief Network to Analyze Time Series Data0
A deep learning architecture to detect events in EEG signals during sleepCode0
Shortening Time Required for Adaptive Structural Learning Method of Deep Belief Network with Multi-Modal Data Arrangement0
Knowledge Extracted from Recurrent Deep Belief Network for Real Time Deterministic Control0
A Recurrent Neural Network Survival Model: Predicting Web User Return TimeCode0
Exploiting statistical dependencies of time series with hierarchical correlation reconstruction0
Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal AnalysisCode0
Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting0
Process Monitoring Using Maximum Sequence Divergence0
Learning The Sequential Temporal Information with Recurrent Neural Networks0
A Variational Time Series Feature Extractor for Action PredictionCode0
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks0
Adversarial Robustness Toolbox v1.0.0Code3
Mining Illegal Insider Trading of Stocks: A Proactive Approach0
Dynamic Prediction Length for Time Series with Sequence to Sequence Networks0
Improving Optimization in Models With Continuous Symmetry Breaking0
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series0
Deep Bayesian Nonparametric Tracking0
Robust and Scalable Models of Microbiome Dynamics0
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 Inference0
Nonlinearity in stock networks0
Multilevel Wavelet Decomposition Network for Interpretable Time Series AnalysisCode0
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention0
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 Detection0
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks0
Multi-variable LSTM neural network for autoregressive exogenous model0
Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-assisted Surgery0
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
A review on distance based time series classification0
The Role of Agricultural Sector Productivity in Economic Growth: The Case of Iran's Economic Development Plan0
Stationary Geometric Graphical Model Selection0
Discovering Signals from Web Sources to Predict Cyber Attacks0
SOM-VAE: Interpretable Discrete Representation Learning on Time SeriesCode0
EigenNetworks0
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified