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 3801–3850 of 6748 papers

TitleStatusHype
SARS-COV-2 Pandemic: Understanding the Impact of Lockdown in the Most Affected States of India—0
Scalable and Hybrid Ensemble-Based Causality Discovery—0
Scalable Deployment of AI Time-series Models for IoT—0
Scalable Discovery of Time-Series Shapelets—0
Scalable Gradients and Variational Inference for Stochastic Differential Equations—0
Scalable Hybrid Hidden Markov Model with Gaussian Process Emission for Sequential Time-series Observations—0
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering—0
Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction—0
Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies—0
Scalable photonic reinforcement learning by time-division multiplexing of laser chaos—0
Scalable Pooled Time Series of Big Video Data from the Deep Web—0
Scalable Predictive Time-Series Analysis of COVID-19: Cases and Fatalities—0
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data—0
Scalable Visualisation of Sentiment and Stance—0
Scaled-Time-Attention Robust Edge Network—0
Scaling up Echo-State Networks with multiple light scattering—0
Scene Learning: Deep Convolutional Networks For Wind Power Prediction by Embedding Turbines into Grid Space—0
SCformer: Segment Correlation Transformer for Long Sequence Time Series Forecasting—0
Scheduling Planting Time Through Developing an Optimization Model and Analysis of Time Series Growing Degree Units—0
Scoring and Assessment in Medical VR Training Simulators with Dynamic Time Series Classification—0
Searching for Biophysically Realistic Parameters for Dynamic Neuron Models by Genetic Algorithms from Calcium Imaging Recording—0
Seasonal-adjustment Based Feature Selection Method for Large-scale Search Engine Logs—0
Seasonal Encoder-Decoder Architecture for Forecasting—0
Seasonality, density dependence and spatial population synchrony—0
Seasonally-Adjusted Auto-Regression of Vector Time Series—0
Seasonal Stochastic Volatility and the Samuelson Effect in Agricultural Futures Markets—0
Sea surface temperature prediction and reconstruction using patch-level neural network representations—0
Second-order difference subspace—0
Second-Order Ultrasound Elastography with L1-norm Spatial Regularization—0
Options as Silver Bullets: Valuation of Term Loans, Inventory Management, Emissions Trading and Insurance Risk Mitigation using Option Theory—0
Security and Interpretability in Automotive Systems—0
Sedentary Behavior Estimation with Hip-worn Accelerometer Data: Segmentation, Classification and Thresholding—0
SeDMiD for Confusion Detection: Uncovering Mind State from Time Series Brain Wave Data—0
Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling—0
Seeking for a fingerprint: analysis of point processes in actigraphy recording—0
See the Near Future: A Short-Term Predictive Methodology to Traffic Load in ITS—0
Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement—0
Segment Parameter Labelling in MCMC Mean-Shift Change Detection—0
Seismic-Net: A Deep Densely Connected Neural Network to Detect Seismic Events—0
Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity?—0
Selecting Data Adaptive Learner from Multiple Deep Learners using Bayesian Networks—0
Selection of entropy based features for the analysis of the Archimedes' spiral applied to essential tremor—0
Selective Cross-Domain Consistency Regularization for Time Series Domain Generalization—0
Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series—0
Self-awareness in intelligent vehicles: Feature based dynamic Bayesian models for abnormality detection—0
Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning—0
Self-contained Beta-with-Spikes Approximation for Inference Under a Wright-Fisher Model—0
Self-Organization in Spontaneous Movements of Neonates generates Self-specifying Sensory Experiences—0
Self-Organizing Maps with Variable Input Length for Motif Discovery and Word Segmentation—0
Self-Similarity Based Time Warping—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