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 3601–3625 of 6748 papers

TitleStatusHype
Robust Inference on Infinite and Growing Dimensional Time Series Regression—0
Structured Bayesian Gaussian process latent variable model—0
Structured Black Box Variational Inference for Latent Time Series Models—0
Structured low-rank matrix completion for forecasting in time series analysis—0
Structure Learning from Time Series with False Discovery Control—0
Structure Learning of Partitioned Markov Networks—0
Structure Parameter Optimized Kernel Based Online Prediction with a Generalized Optimization Strategy for Nonstationary Time Series—0
STS Classification with Dual-stream CNN—0
Study of non-linear viscoelastic behavior of the human red blood cell—0
Study of Set-Membership Adaptive Kernel Algorithms—0
Study of the impact of climate change on precipitation in Paris area using method based on iterative multiscale dynamic time warping (IMS-DTW)—0
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling—0
Style Miner: Find Significant and Stable Explanatory Factors in Time Series with Constrained Reinforcement Learning—0
StyleTime: Style Transfer for Synthetic Time Series Generation—0
GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences—0
Subjective Metrics-based Cloud Market Performance Prediction—0
Sub-Optimal Multi-Phase Path Planning: A Method for Solving Rubik's Revenge—0
Subset Selection and Summarization in Sequential Data—0
Subspace based low rank and joint sparse matrix recovery—0
Subspace Change-Point Detection via Low-Rank Matrix Factorisation—0
Sufficient Forecasting Using Factor Models—0
Summary Markov Models for Event Sequences—0
SummerTime: Variable-length Time SeriesSummarization with Applications to PhysicalActivity Analysis—0
Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory—0
SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems—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