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 5451–5475 of 6748 papers

TitleStatusHype
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems—0
Scene Learning: Deep Convolutional Networks For Wind Power Prediction by Embedding Turbines into Grid Space—0
Time Series Deinterleaving of DNS Traffic—0
Assessment of electrical and infrastructure recovery in Puerto Rico following hurricane Maria using a multisource time series of satellite imagery—0
Analysis of Advisor Portfolio using Multivariate Time Series and Cosine Similarity—0
Tracking the Evolution of Words with Time-reflective Text Representations—0
Inferring Multidimensional Rates of Aging from Cross-Sectional DataCode0
SVD-based Visualisation and Approximation for Time Series Data in Smart Energy Systems—0
A Stochastic Hybrid Framework for Driver Behavior Modeling Based on Hierarchical Dirichlet Process—0
Adaptive Learning Method of Recurrent Temporal Deep Belief Network to Analyze Time Series Data—0
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 Arrangement—0
Knowledge Extracted from Recurrent Deep Belief Network for Real Time Deterministic Control—0
A Recurrent Neural Network Survival Model: Predicting Web User Return TimeCode0
Exploiting statistical dependencies of time series with hierarchical correlation reconstruction—0
Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal AnalysisCode0
Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting—0
Process Monitoring Using Maximum Sequence Divergence—0
Learning The Sequential Temporal Information with Recurrent Neural Networks—0
A Variational Time Series Feature Extractor for Action PredictionCode0
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks—0
Adversarial Robustness Toolbox v1.0.0Code3
Mining Illegal Insider Trading of Stocks: A Proactive Approach—0
Dynamic Prediction Length for Time Series with Sequence to Sequence Networks—0
Improving Optimization in Models With Continuous Symmetry Breaking—0
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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