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

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Showing 6251–6300 of 6748 papers

TitleStatusHype
ReD-SFA: Relation Discovery Based Slow Feature Analysis for Trajectory Clustering—0
Solving Temporal Puzzles—0
Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks—0
Variational Bayesian Inference for Hidden Markov Models With Multivariate Gaussian Output Distributions—0
Foreign exchange risk premia: from traditional to state-space analyses—0
Linear Credit Risk Models—0
Initial conditions in the neural field model—0
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICACode1
Nonlinear trend removal should be carefully performed in heart rate variability analysis—0
Gaussian variational approximation with sparse precision matrices—0
Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate—0
Learning zero-cost portfolio selection with pattern matching—0
With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams—0
Direct Method for Training Feed-forward Neural Networks using Batch Extended Kalman Filter for Multi-Step-Ahead Predictions—0
Clustering Time Series and the Surprising Robustness of HMMs—0
Inference of High-dimensional Autoregressive Generalized Linear Models—0
Brain Emotional Learning-Based Prediction Model (For Long-Term Chaotic Prediction Applications)—0
ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines—0
Temporal Clustering of Time Series via Threshold Autoregressive Models: Application to Commodity Prices—0
Analyzing Time Series Changes of Correlation between Market Share and Concerns on Companies measured through Search Engine Suggests—0
Forecasting Emerging Trends from Scientific Literature—0
Optimal Transport vs. Fisher-Rao distance between Copulas for Clustering Multivariate Time Series—0
Temporal Taylor's scaling of facial electromyography and electrodermal activity in the course of emotional stimulation—0
Unsupervised Representation Learning of Structured Radio Communication SignalsCode0
Estimating 3D Trajectories from 2D Projections via Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machines—0
Cognitive state classification using transformed fMRI data—0
Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data—0
On stabilizing the variance of dynamic functional brain connectivity time series—0
Dynamic process fault prediction using canonical variable trend analysisCode0
Hyperinflation in Brazil, Israel, and Nicaragua revisited—0
Leveraging Network Dynamics for Improved Link Prediction—0
Hierarchical Quickest Change Detection via Surrogates—0
State-space models' dirty little secrets: even simple linear Gaussian models can have estimation problems—0
Analysis of Blink Rate Variability during reading and memory testing—0
GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model—0
On clustering financial time series: a need for distances between dependent random variables—0
Optimal trading strategies - a time series approach—0
Exact Bayesian inference for off-line change-point detection in tree-structured graphical models—0
Clustering Time-Series Energy Data from Smart Meters—0
Skill-Based Differences in Spatio-Temporal Team Behavior in Defence of The Ancients 2—0
On the Theory and Practice of Privacy-Preserving Bayesian Data Analysis—0
Multi-Scale Convolutional Neural Networks for Time Series ClassificationCode0
Action-Affect Classification and Morphing using Multi-Task Representation Learning—0
Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes—0
The dual frequency RV-coupling coefficient: a novel measure for quantifying cross-frequency information transactions in the brain—0
Short-term time series prediction using Hilbert space embeddings of autoregressive processes—0
Persistent Homology of Attractors For Action Recognition—0
Evaluation and Ensembling of Methods for Reverse Engineering of Brain Connectivity from Imaging Data—0
Modeling Time Series Similarity with Siamese Recurrent Networks—0
Learning Network of Multivariate Hawkes Processes: A Time Series Approach—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