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 4501–4550 of 6748 papers

TitleStatusHype
Laplacian Convolutional Representation for Traffic Time Series Imputation—0
Large Language Models are Few-shot Multivariate Time Series Classifiers—0
Large Language models for Time Series Analysis: Techniques, Applications, and Challenges—0
Estimation of Large Financial Covariances: A Cross-Validation Approach—0
Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)—0
Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing—0
Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI—0
Large-scale kernelized GRANGER causality to infer topology of directed graphs with applications to brain networks—0
Scalable Learning in Reproducing Kernel Krein Spaces—0
Large-scale nonlinear Granger causality: A data-driven, multivariate approach to recovering directed networks from short time-series data—0
Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data—0
Large-Scale Simulation of Multi-Asset Ising Financial Markets—0
Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks—0
Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping—0
Large Spectral Density Matrix Estimation by Thresholding—0
Lasso Guarantees for Time Series Estimation Under Subgaussian Tails and β-Mixing—0
Lasso Inference for High-Dimensional Time Series—0
lassopack: Model selection and prediction with regularized regression in Stata—0
LAST SToP For Modeling Asynchronous Time Series—0
Latent Convergent Cross Mapping—0
Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids—0
Seasonal and Secular Periodicities Identified in the Dynamics of US FDA Medical Devices (1976 2020) Portends Intrinsic Industrial Transformation and Independence of Certain Crises—0
Latent Space Semi-Supervised Time Series Data Clustering—0
Latent Space Unsupervised Semantic Segmentation—0
Latent Temporal Flows for Multivariate Analysis of Wearables Data—0
Latent Time-Adaptive Drift-Diffusion Model—0
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm—0
LatTe Flows: Latent Temporal Flows for Multivariate Sequence Analysis—0
LAVARNET: Neural Network Modeling of Causal Variable Relationships for Multivariate Time Series Forecasting—0
LB-SimTSC: An Efficient Similarity-Aware Graph Neural Network for Semi-Supervised Time Series Classification—0
Lead-lag detection and network clustering for multivariate time series with an application to the US equity market—0
Leapfrogging of a deterministic model for microeconomic systems in competitive markets—0
Large-step neural network for learning the symplectic evolution from partitioned data—0
Learning Auto-regressive Models from Sequence and Non-sequence Data—0
Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders—0
Learning-based estimation of in-situ wind speed from underwater acoustics—0
Learning-Based Real-Time Event Identification Using Rich Real PMU Data—0
Learning Beyond Similarities: Incorporating Dissimilarities between Positive Pairs in Self-Supervised Time Series Learning—0
Learning binary or real-valued time-series via spike-timing dependent plasticity—0
Learning Causality: Synthesis of Large-Scale Causal Networks from High-Dimensional Time Series Data—0
Learning Causally-Generated Stationary Time Series—0
Learning code summarization from a small and local dataset—0
Learning Compact Physics-Aware Delayed Photocurrent Models Using Dynamic Mode Decomposition—0
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior—0
Learning conditional independence structure for high-dimensional uncorrelated vector processes—0
Learning Continuous-Time Dynamics by Stochastic Differential Networks—0
Learning convolution filters for inverse covariance estimation of neural network connectivity—0
Learning Correlation Space for Time Series—0
Learning Deep Representations from Clinical Data for Chronic Kidney Disease—0
Learning Differential Operators for Interpretable Time Series Modeling—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