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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

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Showing 6651–6700 of 6748 papers

TitleStatusHype
Joint modeling of multiple time series via the beta process with application to motion capture segmentation—0
Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting—0
Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method—0
Nonlinear Time Series Modeling: A Unified Perspective, Algorithm, and Application—0
Word Recognition from Continuous Articulatory Movement Time-series Data using Symbolic Representations—0
Generating Student Feedback from Time-Series Data Using Reinforcement Learning—0
Exploiting Topic based Twitter Sentiment for Stock Prediction—0
A user-centric model of voting intention from Social Media—0
Time-Series Classification Through Histograms of Symbolic Polynomials—0
Multi-horizon solar radiation forecasting for Mediterranean locations using time series models—0
Deriving land surface phenology indicators from CO2 eddy covariance measurements—0
Gaussian Process Conditional Copulas with Applications to Financial Time Series—0
A Direct Estimation of High Dimensional Stationary Vector Autoregressions—0
Sparse Principal Component Analysis for High Dimensional Vector Autoregressive Models—0
Frequency-Domain Stochastic Modeling of Stationary Bivariate or Complex-Valued Signals—0
Sparse Auto-Regressive: Robust Estimation of AR Parameters—0
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC—0
Emotional Expression Classification using Time-Series Kernels—0
Motif Detection Inspired by Immune Memory (JORS)—0
Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization—0
Dynamic Covariance Models for Multivariate Financial Time Series—0
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming—0
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data—0
Identifying Pairs in Simulated Bio-Medical Time-Series—0
Fractal structures in Adversarial Prediction—0
Unsupervised model-free representation learning—0
Learning Heteroscedastic Models by Convex Programming under Group Sparsity—0
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures—0
Highly comparative time-series analysis: The empirical structure of time series and their methodsCode1
Towards The Inductive Acquisition of Temporal Knowledge—0
A dependent partition-valued process for multitask clustering and time evolving network modelling—0
An Introductory Study on Time Series Modeling and Forecasting—0
Structure Discovery in Nonparametric Regression through Compositional Kernel SearchCode0
A Latent Source Model for Nonparametric Time Series Classification—0
Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory—0
Macro-Economic Time Series Modeling and Interaction Networks—0
Nonparametric risk bounds for time-series forecasting—0
Modelling Reciprocating Relationships with Hawkes Processes—0
Identification of Recurrent Patterns in the Activation of Brain Networks—0
Locating Changes in Highly Dependent Data with Unknown Number of Change Points—0
Patient Risk Stratification for Hospital-Associated C. diff as a Time-Series Classification Task—0
Expectation Propagation in Gaussian Process Dynamical Systems—0
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data—0
Optimally fuzzy temporal memory—0
Cross-Lingual Topic Alignment in Time Series Japanese / Chinese News—0
Reducing statistical time-series problems to binary classification—0
Locally adaptive factor processes for multivariate time series—0
Online Learning with Predictable Sequences—0
Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version—0
Identifying Constant and Unique Relations by using Time-Series Text—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