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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 2201–2225 of 6748 papers

TitleStatusHype
Adaptive Kernel Estimation of the Spectral Density with Boundary Kernel Analysis—0
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series—0
Cluster-and-Conquer: A Framework For Time-Series Forecasting—0
Applying Deep Learning to Detect Traffic Accidents in Real Time Using Spatiotemporal Sequential Data—0
CLPVG: Circular limited penetrable visibility graph as a new network model for time series—0
Cloud Cover Nowcasting with Deep Learning—0
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction—0
Entropy and Transfer Entropy: The Dow Jones and the build up to the 1997 Asian Crisis—0
Entropy-based Discovery of Summary Causal Graphs in Time Series—0
Equivalence relations and L^p distances between time series with application to the Black Summer Australian bushfires—0
Estimating 3D Trajectories from 2D Projections via Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machines—0
Evolving Gaussian Process kernels from elementary mathematical expressions—0
Closed-form Inference and Prediction in Gaussian Process State-Space Models—0
Aligned Multi-Task Gaussian Process—0
Applications of Signature Methods to Market Anomaly Detection—0
Adaptive Inducing Points Selection For Gaussian Processes—0
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach—0
CLeaR: An Adaptive Continual Learning Framework for Regression Tasks—0
Applications of shapelet transform to time series classification of earthquake, wind and wave data—0
Applications of Online Nonnegative Matrix Factorization to Image and Time-Series Data—0
Class-Specific Attention (CSA) for Time-Series Classification—0
A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics—0
EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid—0
Ensemble of Hankel Matrices for Face Emotion Recognition—0
Classifying Pattern and Feature Properties to Get a Θ(n) Checker and Reformulation for Sliding Time-Series Constraints—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