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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 2151–2200 of 6748 papers

TitleStatusHype
5G Traffic Prediction with Time Series Analysis—0
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction—0
Approximation algorithms for confidence bands for time series—0
CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation—0
A Statistical Recurrent Stochastic Volatility Model for Stock Markets—0
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data—0
Structural clustering of volatility regimes for dynamic trading strategies—0
Approximating DTW with a convolutional neural network on EEG data—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
Clustering Time-Series Energy Data from Smart Meters—0
A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction—0
Clustering Time Series Data through Autoencoder-based Deep Learning Models—0
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization—0
Approximate Newton-based statistical inference using only stochastic gradients—0
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
Clustering Time Series and the Surprising Robustness of HMMs—0
Clustering piecewise stationary processes—0
Approximate Joint Diagonalization and Geometric Mean of Symmetric Positive Definite Matrices—0
Clustering of Time Series Data with Prior Geographical Information—0
Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples—0
Approximate Extraction of Late-Time Returns via Morphological Component Analysis—0
ACCTS: an Adaptive Model Training Policy for Continuous Classification of Time Series—0
Approximate Collapsed Gibbs Clustering with Expectation Propagation—0
tsmp: An R Package for Time Series with Matrix Profile—0
Clustering Macroeconomic Time Series—0
Clustering Interval-Censored Time-Series for Disease Phenotyping—0
Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Data—0
Enhancing Energy System Models Using Better Load Forecasts—0
Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study—0
Clustering individuals based on multivariate EMA time-series data—0
Clustering high dimensional meteorological scenarios: results and performance index—0
Approximate Bayes factors for unit root testing—0
Clustering Gene Expression Time Series with Coregionalization: Speed propagation of ALS—0
Clustering Financial Time Series: How Long is Enough?—0
Approaches and Applications of Early Classification of Time Series: A Review—0
All-Clear Flare Prediction Using Interval-based Time Series Classifiers—0
Clustering evolving data using kernel-based methods—0
Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference—0
All-atom Molecular Dynamics Simulations of the Projection Domain of the Intrinsically Disordered htau40 Protein—0
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration—0
Clustering Discrete-Valued Time Series—0
Applying Regression Conformal Prediction with Nearest Neighbors to time series data—0
Adaptive Learning Method of Recurrent Temporal Deep Belief Network to Analyze Time Series Data—0
Enhance the performance of navigation: A two-stage machine learning approach—0
Applying Nature-Inspired Optimization Algorithms for Selecting Important Timestamps to Reduce Time Series Dimensionality—0
Clustering Activity-Travel Behavior Time Series using Topological Data Analysis—0
A Linear Transportation L^p Distance for Pattern Recognition—0
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures—0
Cluster-based Feature Importance Learning for Electronic Health Record Time-series—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