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 2701–2750 of 6748 papers

TitleStatusHype
Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings—0
EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid—0
Hankel-structured Tensor Robust PCA for Multivariate Traffic Time Series Anomaly Detection—0
Cross-modal Knowledge Distillation for Vision-to-Sensor Action RecognitionCode0
Subspace Change-Point Detection via Low-Rank Matrix Factorisation—0
Joint Normality Test Via Two-Dimensional Projection—0
Generative Pre-Trained Transformer for Cardiac Abnormality Detection—0
Predictive Maintenance for General Aviation Using Convolutional Transformers—0
Automated Testing of AI Models—0
Darts: User-Friendly Modern Machine Learning for Time Series—0
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data—0
5G Traffic Prediction with Time Series Analysis—0
PRRS Outbreak Prediction via Deep Switching Auto-Regressive Factorization Modeling—0
Time Series Forecasting Using Manifold Learning—0
Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop MappingCode0
EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia—0
An Accurate Smartphone Battery Parameter Calibration Using Unscented Kalman Filter—0
Generative Optimization Networks for Memory Efficient Data GenerationCode0
Bayesian autoregressive spectral estimation—0
Interpreting intermediate convolutional layers in unsupervised acoustic word classification—0
Attention Augmented Convolutional Transformer for Tabular Time-series—0
Networked Time Series Prediction with Incomplete Data via Generative Adversarial Network—0
RapidAI4EO: A Corpus for Higher Spatial and Temporal Reasoning—0
Overcoming limited battery data challenges: A coupled neural network approach—0
Cross-Modal Virtual Sensing for Combustion Instability Monitoring—0
Causality and Generalizability: Identifiability and Learning Methods—0
HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization—0
State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting—0
Classification of Time-Series Data Using Boosted Decision TreesCode0
Empirical Quantitative Analysis of COVID-19 Forecasting Models—0
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium PropagationCode0
Multi Scale Graph Wavenet for Wind Speed Forecasting—0
Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Series—0
Feature Selection on a Flare Forecasting Testbed: A Comparative Study of 24 MethodsCode0
PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series—0
Uncertainty, volatility and the persistence norms of financial time series—0
LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values—0
Neural Markov Controlled SDE: Stochastic Optimization for Continuous-Time Data—0
Integrating Attention Feedback into the Recurrent Neural NetworkCode0
Taking ROCKET on an efficiency mission: A distributed solution for fast and accurate multivariate time series classification—0
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Representation Learning in Time Series—0
Multi-Task Processes—0
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis—0
Deep Neural Networks on EEG signals to predict Attention Score using Gramian Angular Difference Field—0
TRAKR – A reservoir-based tool for fast and accurate classification of neural time-series patterns—0
Automatic Forecasting via Meta-Learning—0
Kernel distance measures for time series, random fields and other structured data—0
Variational Inference for Continuous-Time Switching Dynamical Systems—0
Cross-Camera Human Motion Transfer by Time Series AnalysisCode0
TAMP-S2GCNets: Coupling Time-Aware Multipersistence Knowledge Representation with Spatio-Supra Graph Convolutional Networks for Time-Series Forecasting—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