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 5851–5900 of 6748 papers

TitleStatusHype
Social Style Characterization from Egocentric Photo-streams—0
Granger Mediation Analysis of Multiple Time Series with an Application to fMRI—0
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning—0
Motif-based Rule Discovery for Predicting Real-valued Time Series—0
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic ForecastingCode1
On Early-stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners—0
Discovering Potential Correlations via Hypercontractivity—0
Cascaded Region-based Densely Connected Network for Event Detection: A Seismic Application—0
Support Spinor Machine—0
Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications—0
R2N2: Residual Recurrent Neural Networks for Multivariate Time Series Forecasting—0
LSTM Fully Convolutional Networks for Time Series ClassificationCode1
RNN-based Early Cyber-Attack Detection for the Tennessee Eastman Process—0
Basic Filters for Convolutional Neural Networks Applied to Music: Training or Design?—0
Deep and Confident Prediction for Time Series at UberCode1
Boosting the kernelized shapelets: Theory and algorithms for local features—0
Tensor Representation in High-Frequency Financial Data for Price Change Prediction—0
Towards social pattern characterization in egocentric photo-streams—0
Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks—0
Classifying Frames at the Sentence Level in News Articles—0
Forecasting Consumer Spending from Purchase Intentions Expressed on Social Media—0
Boosting Information Extraction Systems with Character-level Neural Networks and Free Noisy Supervision—0
Can Agent-Based Models Probe Market Microstructure?—0
A State-Space Approach to Dynamic Nonnegative Matrix Factorization—0
Interpretable Categorization of Heterogeneous Time Series Data—0
On the Reconstruction Risk of Convolutional Sparse Dictionary LearningCode0
m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series—0
Explainable Recommendation: Theory and Applications—0
Boltzmann machines for time-series—0
An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection—0
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction—0
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction—0
Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches—0
Stock Price Prediction via Discovering Multi-Frequency Trading PatternsCode4
Some stylized facts of the Bitcoin market—0
Effects of pitch and timing expectancy on musical emotion—0
Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1—0
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data—0
Hypotheses testing on infinite random graphs—0
Anomaly Detection on Graph Time Series—0
Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS DiagnosisCode0
Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model—0
Bidirectional learning for time-series models with hidden units—0
Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates—0
Coherent probabilistic forecasts for hierarchical time series—0
Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference—0
Detecting Changes in Twitter Streams using Temporal Clusters of Hashtags—0
Tensorial Recurrent Neural Networks for Longitudinal Data Analysis—0
Spurious memory in non-equilibrium stochastic models of imitative behavior—0
A Comparative Study of the Clinical use of Motion Analysis from Kinect Skeleton Data—0
Show:102550
← PrevPage 118 of 135Next →
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