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 1551–1600 of 6748 papers

TitleStatusHype
Time series quantile regression using random forests—0
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion—0
Topological biomarkers for real-time detection of epileptic seizures—0
Deep learning for structural health monitoring: An application to heritage structures—0
Collaborative Multiobjective Evolutionary Algorithms in search of better Pareto Fronts. An application to trading systems—0
Reservoir Computing via Quantum Recurrent Neural Networks—0
Boosted p-Values for High-Dimensional Vector Autoregression—0
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework—0
Robust Time Series Chain Discovery with Incremental Nearest Neighbors—0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
MUSTACHE: Multi-Step-Ahead Predictions for Cache Eviction—0
On Estimation and Inference of Large Approximate Dynamic Factor Models via the Principal Component Analysis—0
Physics-inspired machine learning for power grid frequency modelling—0
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds—0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
Infinite-Dimensional Adaptive Boundary Observer for Inner-Domain Temperature Estimation of 3D Electrosurgical Processes using Surface Thermography Sensing—0
Evaluating Impact of Social Media Posts by Executives on Stock PricesCode0
Recurrent Neural Networks and Universal Approximation of Bayesian Filters—0
A novel approach to quantify volatility prediction—0
Denoising neural networks for magnetic resonance spectroscopy—0
Variational Inference Aided Estimation of Time Varying Channels—0
Ensemble transport smoothing. Part I: Unified frameworkCode0
MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs—0
Uncertainty-DTW for Time Series and SequencesCode0
AtOMICS: A neural network-based Automated Optomechanical Intelligent Coupling System for testing and characterization of silicon photonics chiplets—0
Monitoring the Dynamic Networks of Stock Returns—0
Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution—0
A Novel Sparse Bayesian Learning and Its Application to Fault Diagnosis for Multistation Assembly Systems—0
Multimodal Estimation of Change Points of Physiological Arousal in DriversCode0
ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks—0
Forecasting Graph Signals with Recursive MIMO Graph Filters—0
Adaptive Estimation of Graphical Models under Total Positivity—0
RapidAI4EO: Mono- and Multi-temporal Deep Learning models for Updating the CORINE Land Cover Product—0
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise—0
Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots—0
Cap or No Cap? What Can Governments Do to Promote EV Sales?—0
Deep Subspace Encoders for Nonlinear System Identification—0
Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes—0
Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data—0
WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting—0
Preparing fMRI Data for Statistical Analysis—0
Novelty Detection in Time Series via Weak Innovations Representation: A Deep Learning Approach—0
Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble—0
Applications of Machine Learning in Pharmacogenomics: Clustering Plasma Concentration-Time Curves—0
Exploring Self-Attention for Crop-type Classification Explainability—0
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior—0
Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram—0
Theoretical analysis of deep neural networks for temporally dependent observations—0
Multimodal Neural Network For Demand Forecasting—0
Irregularly-Sampled Time Series Modeling with Spline Networks—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