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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 4651–4700 of 6748 papers

TitleStatusHype
User Engagement in Mobile Health Applications—0
U-shaped Transformer: Retain High Frequency Context in Time Series Analysis—0
Using an Ancillary Neural Network to Capture Weekends and Holidays in an Adjoint Neural Network Architecture for Intelligent Building Management—0
Using Autoencoders To Learn Interesting Features For Detecting Surveillance Aircraft—0
Using Bayesian Dynamical Systems for Motion Template Libraries—0
Using Clinical Notes for ICU Management—0
Using CNNs for AD classification based on spatial correlation of BOLD signals during the observation—0
Using Conceptual Spaces to Model Domain Knowledge in Data-to-Text Systems—0
Using Connectome Features to Constrain Echo State Networks—0
Using Deep Learning for price prediction by exploiting stationary limit order book features—0
Using Deep Learning to Correlate Reddit Posts with Economic Time Series During the COVID-19 Pandemic—0
Using deep neural networks to improve the precision of fast-sampled particle timing detectors—0
Using Detailed Access Trajectories for Learning Behavior Analysis—0
Using Entropy Measures for Monitoring the Evolution of Activity Patterns—0
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets—0
Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems—0
Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links—0
Using Machine Learning to Predict Realized Variance—0
Using Neural Networks by Modelling Semi-Active Shock Absorber—0
Using Quantum Mechanics to Cluster Time Series—0
Using Spatio-temporal Deep Learning for Forecasting Demand and Supply-demand Gap in Ride-hailing System with Anonymised Spatial Adjacency Information—0
Using Synthetic Data to Improve the Long-range Forecasting of Time Series Data—0
Using Temporal and Topological Features for Intrusion Detection in Operational Networks—0
Using the quantization error from Self-Organized Map (SOM) output for detecting critical variability in large bodies of image time series in less than a minute—0
Using time series and natural language processing to identify viral moments in the 2016 U.S. Presidential Debate—0
Utilizing Temporal Information for Taxonomy Construction—0
UTSD: Unified Time Series Diffusion Model—0
Vaccination strategies and transmission of COVID-19: evidence across advanced countries—0
VAE-LIME: Deep Generative Model Based Approach for Local Data-Driven Model Interpretability Applied to the Ironmaking Industry—0
Validation Methods for Energy Time Series Scenarios from Deep Generative Models—0
Valuing Players Over Time—0
Variable and Fixed Interval Exponential Smoothing—0
Variance of entropy for testing time-varying regimes with an application to meme stocks—0
Variance Reduced Training with Stratified Sampling for Forecasting Models—0
Variational approach for learning Markov processes from time series data—0
Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series—0
Variational Bayesian Inference for Hidden Markov Models With Multivariate Gaussian Output Distributions—0
Variational Bayesian inference of hidden stochastic processes with unknown parameters—0
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition—0
Variational Dynamic Mixtures—0
Variational Gaussian Process Dynamical Systems—0
Variational Gaussian Process State-Space Models—0
Variational Hyper RNN for Sequence Modeling—0
Variational Inference Aided Estimation of Time Varying Channels—0
Variational Inference for Continuous-Time Switching Dynamical Systems—0
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network—0
Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series—0
Variational inference for rare variant detection in deep, heterogeneous next-generation sequencing data—0
Variational inference of latent state sequences using Recurrent Networks—0
Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps—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