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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4526–4550 of 6748 papers

TitleStatusHype
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