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

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Showing 2226–2250 of 6748 papers

TitleStatusHype
Deep Metric Learning with Locality Sensitive Angular Loss for Self-Correcting Source Separation of Neural Spiking Signals—0
Bundle Networks: Fiber Bundles, Local Trivializations, and a Generative Approach to Exploring Many-to-one MapsCode0
Detecting Slag Formations with Deep Convolutional Neural Networks—0
Ousiometrics and Telegnomics: The essence of meaning conforms to a two-dimensional powerful-weak and dangerous-safe framework with diverse corpora presenting a safety biasCode0
Dynamical Wasserstein Barycenters for Time-series ModelingCode1
Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells—0
Real-time Drift Detection on Time-series Data—0
Causal Discovery from Conditionally Stationary Time Series—0
A Multi-scale Time-series Dataset with Benchmark for Machine Learning in Decarbonized Energy GridsCode1
Time Series Analysis via Network Science: Concepts and Algorithms—0
Novel Features for Time Series Analysis: A Complex Networks ApproachCode1
Role of assortativity in predicting burst synchronization using echo state network—0
TCube: Domain-Agnostic Neural Time-series NarrationCode1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
Graph-Guided Network for Irregularly Sampled Multivariate Time SeriesCode1
Time Series Classification Using Convolutional Neural Network On Imbalanced Datasets—0
Long Expressive Memory for Sequence ModelingCode1
Nonparametric Tests of Conditional Independence for Time Series—0
Probabilistic prediction of the heave motions of a semi-submersible by a deep learning problem modelCode0
EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid—0
Space-Time-Separable Graph Convolutional Network for Pose ForecastingCode1
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings—0
Cross-modal Knowledge Distillation for Vision-to-Sensor Action RecognitionCode0
Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection—0
Joint Normality Test Via Two-Dimensional Projection—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