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 1751–1800 of 6748 papers

TitleStatusHype
Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series ForecastingCode1
DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization—0
Macroeconomic Effect of Uncertainty and Financial Shocks: a non-Gaussian VAR approach—0
Combating Distribution Shift for Accurate Time Series Forecasting via HypernetworksCode1
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed GraphsCode1
Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks—0
Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods—0
Estimation of Evaporator Valve Sizes in Supermarket Refrigeration Cabinets—0
A Deep Learning Model for Forecasting Global Monthly Mean Sea Surface Temperature Anomalies—0
Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection—0
Time Series Analysis of Blockchain-Based Cryptocurrency Price ChangesCode0
A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network—0
Long Run Risk in Stationary Structural Vector Autoregressive Models—0
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting—0
PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting—0
Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models—0
Signal Decomposition Using Masked Proximal OperatorsCode1
"Back to the future" projections for COVID-19 surgesCode0
Ensemble Conformalized Quantile Regression for Probabilistic Time Series ForecastingCode1
SAITS: Self-Attention-based Imputation for Time SeriesCode0
Level set based particle filter driven by optical flow: an application to track the salt boundary from X-ray CT time-series—0
Multi-Objective Model Selection for Time Series Forecasting—0
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs—0
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEsCode1
Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition—0
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time SeriesCode3
Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition—0
A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation—0
HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing—0
TimeREISE: Time-series Randomized Evolving Input Sample Explanation—0
Domain Adaptation with Representation Learning and Nonlinear Relation for Time SeriesCode0
Market-Based Price Autocorrelation—0
LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data—0
Investigating the genomic background of CRISPR-Cas genomes for CRISPR-based antimicrobials—0
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly DetectionCode1
ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformer—0
Adaptive Conformal Predictions for Time SeriesCode1
Transformers in Time Series: A SurveyCode4
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens—0
Simple Models and Biased Forecasts—0
Sequential Monte Carlo With Model Tempering—0
Recurrent Neural Networks for Dynamical Systems: Applications to Ordinary Differential Equations, Collective Motion, and Hydrological Modeling—0
Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inference—0
Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detection—0
Feature Construction and Selection for PV Solar Power Modeling—0
Local approximation of operators—0
Flowformer: Linearizing Transformers with Conservation FlowsCode2
Motion Correction and Volumetric Reconstruction for Fetal Functional Magnetic Resonance Imaging DataCode1
Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data—0
Fitting Sparse Markov Models to Categorical Time Series Using Regularization—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