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 5551–5575 of 6748 papers

TitleStatusHype
Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies—0
Approximate Newton-based statistical inference using only stochastic gradients—0
Model Selection in Time Series Analysis: Using Information Criteria as an Alternative to Hypothesis Testing—0
Machine-learning inference of fluid variables from data using reservoir computing—0
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive LearningCode0
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits—0
Structured Bayesian Gaussian process latent variable model—0
NEWMA: a new method for scalable model-free online change-point detectionCode0
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopyCode0
STS Classification with Dual-stream CNN—0
On Attention Models for Human Activity Recognition—0
Deep Generative Markov State ModelsCode0
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference—0
Taxi demand forecasting: A HEDGE based tessellation strategy for improved accuracy—0
Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions—0
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives—0
Robust and Scalable Models of Microbiome Dynamics—0
Improved Predictive Models for Acute Kidney Injury with IDEAs: Intraoperative Data Embedded Analytics—0
Structural Breaks in Time Series—0
Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective—0
An Unsupervised Clustering-Based Short-Term Solar Forecasting Methodology Using Multi-Model Machine Learning Blending—0
Towards a universal neural network encoder for time series—0
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders—0
Foundations of Sequence-to-Sequence Modeling for Time Series—0
Real-time regression analysis with deep convolutional neural networksCode0
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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