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 3051–3075 of 6748 papers

TitleStatusHype
Hierarchical Linear Dynamical System for Representing Notes from Recorded Audio—0
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data—0
Hierarchically Regularized Deep Forecasting—0
Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification—0
Frequency-Domain Stochastic Modeling of Stationary Bivariate or Complex-Valued Signals—0
Frequency Domain Compact 3D Convolutional Neural Networks—0
Hierarchical Quickest Change Detection via Surrogates—0
Hierarchical regularization networks for sparsification based learning on noisy datasets—0
Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression—0
Hierarchical Symbolic Dynamic Filtering of Streaming Non-stationary Time Series Data—0
Arm order recognition in multi-armed bandit problem with laser chaos time series—0
High Dimensional Forecasting via Interpretable Vector Autoregression—0
A minor extension of the logistic equation for growth of word counts on online media: Parametric description of diversity of growth phenomena in society—0
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems—0
HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions—0
Free congruence: an exploration of expanded similarity measures for time series data—0
High dimensional Bayesian Optimization Algorithm for Complex System in Time Series—0
High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series—0
High-Dimensional Granger Causality for Climatic Attribution—0
Computer activity learning from system call time series—0
High-dimensional mixed-frequency IV regression—0
FreDo: Frequency Domain-based Long-Term Time Series Forecasting—0
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases—0
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting—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