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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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5701–5750 of 6748 papers

TitleStatusHype
Classification with the matrix-variate-t distribution—0
Classifiers With a Reject Option for Early Time-Series Classification—0
Classifying Contaminated Cell Cultures using Time Series Features—0
Classifying Frames at the Sentence Level in News Articles—0
Classifying Human Activities using Machine Learning and Deep Learning Techniques—0
Classifying Image Sequences of Astronomical Transients with Deep Neural Networks—0
Classifying Pattern and Feature Properties to Get a Θ(n) Checker and Reformulation for Sliding Time-Series Constraints—0
Class-Specific Attention (CSA) for Time-Series Classification—0
CLeaR: An Adaptive Continual Learning Framework for Regression Tasks—0
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach—0
Closed-form Inference and Prediction in Gaussian Process State-Space Models—0
Cloud Cover Nowcasting with Deep Learning—0
CLPVG: Circular limited penetrable visibility graph as a new network model for time series—0
Cluster-and-Conquer: A Framework For Time-Series Forecasting—0
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series—0
Cluster-based Feature Importance Learning for Electronic Health Record Time-series—0
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures—0
Clustering Activity-Travel Behavior Time Series using Topological Data Analysis—0
Clustering Discrete-Valued Time Series—0
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration—0
Clustering evolving data using kernel-based methods—0
Clustering Financial Time Series: How Long is Enough?—0
Clustering Gene Expression Time Series with Coregionalization: Speed propagation of ALS—0
Clustering high dimensional meteorological scenarios: results and performance index—0
Clustering individuals based on multivariate EMA time-series data—0
Clustering Interval-Censored Time-Series for Disease Phenotyping—0
Clustering Macroeconomic Time Series—0
Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples—0
Clustering of Time Series Data with Prior Geographical Information—0
Clustering piecewise stationary processes—0
Clustering Time Series and the Surprising Robustness of HMMs—0
Clustering Time-Series by a Novel Slope-Based Similarity Measure Considering Particle Swarm Optimization—0
Clustering Time Series Data through Autoencoder-based Deep Learning Models—0
Clustering Time-Series Energy Data from Smart Meters—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
Structural clustering of volatility regimes for dynamic trading strategies—0
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data—0
CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation—0
COBRAS-TS: A new approach to Semi-Supervised Clustering of Time Series—0
Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning—0
Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model—0
Co-eye: A Multi-resolution Symbolic Representation to TimeSeries Diversified Ensemble Classification—0
Cognitive Computing to Optimize IT Services—0
Cognitive forces shape the dynamics of word usage across multiple languages—0
Cognitive state classification using transformed fMRI data—0
Coherence-based Label Propagation over Time Series for Accelerated Active Learning—0
Coherent probabilistic forecasts for hierarchical time series—0
COHORTNEY: Non-Parametric Clustering of Event Sequences—0
Coincident Learning for Unsupervised Anomaly Detection—0
Cointegration of SARS-CoV-2 Transmission with Weather Conditions and Mobility during the First Year of the COVID-19 Pandemic in the United States—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