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 4601–4650 of 6748 papers

TitleStatusHype
Learning the Exact Topology of Undirected Consensus Networks—0
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding—0
Learning the Non-linearity in Convolutional Neural Networks—0
Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics—0
Learning Theory and Algorithms for Forecasting Non-stationary Time Series—0
Learning The Sequential Temporal Information with Recurrent Neural Networks—0
Learning Through Limited Self-Supervision: Improving Time-Series Classification Without Additional Data via Auxiliary Tasks—0
Learning Time Series Detection Models from Temporally Imprecise Labels—0
Learning Time Series from Scale Information—0
Learning to Adaptively Scale Recurrent Neural Networks—0
Learning to Attack Powergrids with DERs—0
Learning to Diagnose with LSTM Recurrent Neural Networks—0
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks—0
Learning to Generate Market Comments from Stock Prices—0
Learning to Predict with Highly Granular Temporal Data: Estimating individual behavioral profiles with smart meter data—0
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks—0
Learning User Intent from Action Sequences on Interactive Systems—0
Predicting waves in fluids with deep neural network—0
Learning Weighted Submanifolds with Variational Autoencoders and Riemannian Variational Autoencoders—0
Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators—0
Learning Wildfire Model from Incomplete State Observations—0
Learning with little mixing—0
Learning Without Mixing: Towards A Sharp Analysis of Linear System Identification—0
Learning zero-cost portfolio selection with pattern matching—0
Learning to Generate Time Series Conditioned Graphs with Generative Adversarial Nets—0
LEAVES: Learning Views for Time-Series Data in Contrastive Learning—0
Lensless Imaging with Compressive Ultrafast Sensing—0
Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures—0
Less is more: Selecting the right benchmarking set of data for time series classification—0
LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning—0
Level Generation with Quantum Reservoir Computing—0
Level set based particle filter driven by optical flow: an application to track the salt boundary from X-ray CT time-series—0
Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale—0
Leveraging Image-based Generative Adversarial Networks for Time Series Generation—0
Leveraging latent persistency in United States patent and trademark applications to gain insight into the evolution of an innovation-driven economy—0
Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media—0
Leveraging Network Dynamics for Improved Link Prediction—0
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models—0
Leveraging Pre-Images to Discover Nonlinear Relationships in Multivariate Environments—0
Leveraging Vision-Language Models for Granular Market Change Prediction—0
LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment—0
Lie Transform--based Neural Networks for Dynamics Simulation and Learning—0
LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values—0
Light-weight Gesture Sensing Using FMCW Radar Time Series Data—0
Limits to causal inference with state-space reconstruction for infectious disease—0
Limit Theorems for Factor Models—0
LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data—0
Linear Credit Risk Models—0
Linear, Machine Learning and Probabilistic Approaches for Time Series Analysis—0
Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series—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