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 3001–3050 of 6748 papers

TitleStatusHype
Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations—0
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks—0
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events—0
Convolutional Mixture Density Recurrent Neural Network for Predicting User Location with WiFi Fingerprints—0
Convolutional Neural Network-Bagged Decision Tree: A hybrid approach to reduce electric vehicle's driver's range anxiety by estimating energy consumption in real-time—0
Guiding Visual Attention in Deep Convolutional Neural Networks Based on Human Eye Movements—0
Half-empty or half-full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime—0
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation—0
Handling Missing Observations with an RNN-based Prediction-Update Cycle—0
Handling temporality of clinical events with application to Adverse Drug Event detection in Electronic Health Records: A scoping review—0
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review—0
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components—0
Happy or grumpy? A Machine Learning Approach to Analyze the Sentiment of Airline Passengers' Tweets—0
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection—0
Harnessing expressive capacity of Machine Learning modeling to represent complex coupling of Earth's auroral space weather regimes—0
Convolutional Sequence Modeling Revisited—0
A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes—0
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning—0
Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks—0
Causal impact of severe events on electricity demand: The case of COVID-19 in Japan—0
HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing—0
End-to-End Radio Traffic Sequence Recognition with Deep Recurrent Neural Networks—0
Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble—0
End-To-End Prediction of Emotion From Heartbeat Data Collected by a Consumer Fitness Tracker—0
HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection—0
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles—0
Heterogeneous Relational Kernel Learning—0
Causal Hidden Markov Model for Time Series Disease Forecasting—0
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series—0
"Hey, that's not an ODE'": Faster ODE Adjoints with 12 Lines of Code—0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
Asset volatility forecasting:The optimal decay parameter in the EWMA model—0
Latent State Inference in a Spatiotemporal Generative Model—0
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting—0
Adaptive Bayesian Sum of Trees Model for Covariate Dependent Spectral Analysis—0
Copy the dynamics using a learning machine—0
A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements—0
End-to-end NILM System Using High Frequency Data and Neural Networks—0
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding—0
Core-Collapse Supernova Gravitational-Wave Search and Deep Learning Classification—0
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
Hierarchical Clustering for Smart Meter Electricity Loads based on Quantile Autocovariances—0
Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis—0
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series—0
Hierarchical Estimation for Effective and Efficient Sampling Graph Neural Network—0
Hierarchical Fisher Kernels for Longitudinal Data—0
Causal Graph Discovery from Self and Mutually Exciting Time Series—0
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization—0
A Novel Framework for Handling Sparse Data in Traffic Forecast—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