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 6401–6425 of 6748 papers

TitleStatusHype
Conditional Risk Minimization for Stochastic Processes—0
Quantifying Emergent Behavior of Autonomous RobotsCode0
Predicting Sector Index Movement with Microblogging Public Mood Time Series on Social Issues—0
Disk storage management for LHCb based on Data Popularity estimator—0
Learning dynamic Boltzmann machines with spike-timing dependent plasticity—0
High-dimensional Time Series Prediction with Missing Values—0
Theoretical Analysis of the Optimal Free Responses of Graph-Based SFA for the Design of Training Graphs—0
Optimal Copula Transport for Clustering Multivariate Time Series—0
Quadratic Hawkes processes for financial prices—0
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases—0
Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks—0
Sparsity-based Correction of Exponential Artifacts—0
Deep Temporal Sigmoid Belief Networks for Sequence ModelingCode0
Measuring multiscaling in financial time-series—0
A sequential approach to calibrate ecosystem models with multiple time series data—0
Production Function of the Mining Sector of Iran—0
Joint multifractal analysis based on the partition function approach: Analytical analysis, numerical simulation and empirical application—0
Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network—0
A proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series—0
Regional and temporal characteristics of bovine tuberculosis of cattle in Great Britain—0
Forecasting Method for Grouped Time Series with the Use of k-Means Algorithm—0
Learning the Number of Autoregressive Mixtures in Time Series Using the Gap Statistics—0
Modeling Tweet Arrival Times using Log-Gaussian Cox Processes—0
Online Supervised Subspace Tracking—0
Reading Documents for Bayesian Online Change Point Detection—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