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 3451–3500 of 6748 papers

TitleStatusHype
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting—0
Skill Analysis with Time Series Image Data—0
Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2—0
sktime: A Unified Interface for Machine Learning with Time Series—0
Sleep Analytics and Online Selective Anomaly Detection—0
SleepNet: Automated Sleep Analysis via Dense Convolutional Neural Network Using Physiological Time Series—0
SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography—0
SleepPriorCL: Contrastive Representation Learning with Prior Knowledge-based Positive Mining and Adaptive Temperature for Sleep Staging—0
Sliding Spectrum Decomposition for Diversified Recommendation—0
Sliding Window Sum Algorithms for Deep Neural Networks—0
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies—0
Slow-varying Dynamics Assisted Temporal Capsule Network for Machinery Remaining Useful Life Estimation—0
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review—0
Smart Journey in Istanbul: A Mobile Application in Smart Cities for Traffic Estimation by Harnessing Time Series—0
Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention—0
SMART: Skeletal Motion Action Recognition aTtack—0
Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting—0
Smoothing Nonlinear Variational Objectives with Sequential Monte Carlo—0
Snippet Policy Network for Multi-class Varied-length ECG Early Classification—0
Social Link Inference via Multi-View Matching Network from Spatio-Temporal Trajectories—0
Social Style Characterization from Egocentric Photo-streams—0
Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned—0
Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in Delay-embedded Space—0
Solar energy production: Short-term forecasting and risk management—0
SolarGAN: Synthetic Annual Solar Irradiance Time Series on Urban Building Facades via Deep Generative Networks—0
Solar Power Time Series Forecasting Utilising Wavelet Coefficients—0
Solving differential equations with unknown constitutive relations as recurrent neural networks—0
Solving Temporal Puzzles—0
Some stylized facts of the Bitcoin market—0
SOMTimeS: Self Organizing Maps for Time Series Clustering and its Application to Serious Illness Conversations—0
Sparse Auto-Regressive: Robust Estimation of AR Parameters—0
Sparse Bayesian State-Space and Time-Varying Parameter Models—0
Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition—0
Sparseformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification—0
Sparse-Group Log-Sum Penalized Graphical Model Learning For Time Series—0
Sparse High-Dimensional Vector Autoregressive Bootstrap—0
Sparse Interval-valued Time Series Modeling with Machine Learning—0
Sparse Linear Dynamical System with Its Application in Multivariate Clinical Time Series—0
Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions—0
Sparse plus low-rank autoregressive identification in neuroimaging time series—0
Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models—0
Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory—0
Sparse-VQ Transformer: An FFN-Free Framework with Vector Quantization for Enhanced Time Series Forecasting—0
Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series—0
Sparsification of the Alignment Path Search Space in Dynamic Time Warping—0
Sparsifying Networks via Subdifferential Inclusion—0
Sparsistent Estimation of Time-Varying Discrete Markov Random Fields—0
Sparsity-based Correction of Exponential Artifacts—0
Spatial heterogeneity analyses identify limitations of epidemic alert systems: Monitoring influenza-like illness in France—0
Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural 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