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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 3751–3775 of 6748 papers

TitleStatusHype
Anomaly Detection for Multivariate Time Series of Exotic Supernovae—0
Model selection in reconciling hierarchical time seriesCode0
A novel method of fuzzy time series forecasting based on interval index number and membership value using support vector machine—0
RDIS: Random Drop Imputation with Self-Training for Incomplete Time Series Data—0
Sampling Theory of Bandlimited Continuous-Time Graph Signals—0
Estimating and backtesting risk under heavy tails—0
Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell CultivationCode0
Variational Dynamic Mixtures—0
DATSING: Data Augmented Time Series Forecasting with Adversarial Domain Adaptation—0
A semi-supervised autoencoder framework for joint generation and classification of breathing—0
The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool—0
Cryptocurrency portfolio optimization with multivariate normal tempered stable processes and Foster-Hart risk—0
A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with Application to COVID-19—0
Ensemble Kalman Variational Objectives: Nonlinear Latent Trajectory Inference with A Hybrid of Variational Inference and Ensemble Kalman FilterCode0
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time SeriesCode0
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series—0
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations—0
Deep Neural Dynamic Bayesian Networks applied to EEG sleep spindles modelingCode0
Improved Predictive Deep Temporal Neural Networks with Trend Filtering—0
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning—0
An Improved Online Penalty Parameter Selection Procedure for _1-Penalized Autoregressive with Exogenous Variables—0
Short-term Wind Speed Forecasting based on LSSVM Optimized by Elitist QPSO—0
Consumer Behaviour in Retail: Next Logical Purchase using Deep Neural Network—0
Graph Deep Factors for Forecasting—0
Reconstruct Anomaly to Normal: Adversarial Learned and Latent Vector-constrained Autoencoder for Time-series Anomaly Detection—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