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 2951–2975 of 6748 papers

TitleStatusHype
Energy-Efficient Seizure Detection Suitable for low-power Applications—0
Granger Causality in Multi-variate Time Series using a Time Ordered Restricted Vector Autoregressive Model—0
A Soft Computing Approach for Selecting and Combining Spectral Bands—0
Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series—0
Granger Mediation Analysis of Multiple Time Series with an Application to fMRI—0
Graph2Seq: Scalable Learning Dynamics for Graphs—0
GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection—0
Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting -- Full version—0
Causal inference for climate change events from satellite image time series using computer vision and deep learning—0
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware—0
Energy consumption forecasting using a stacked nonparametric Bayesian approach—0
Graph-based era segmentation of international financial integration—0
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components—0
Graph-based Predictable Feature Analysis—0
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks—0
Graph Anomaly Detection in Time Series: A Survey—0
A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes—0
Graph Deep Factors for Forecasting—0
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning—0
Graph Filters for Signal Processing and Machine Learning on Graphs—0
Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks—0
Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction—0
Graphical estimation of multivariate count time series—0
Graphical LASSO Based Model Selection for Time Series—0
Causal impact of severe events on electricity demand: The case of COVID-19 in Japan—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