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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
Multivariate LSTM-FCNs for Time Series ClassificationCode1
Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification—0
Cost-Sensitive Convolution based Neural Networks for Imbalanced Time-Series Classification—0
Non Intrusive Load Monitoring in Chaotic Switching Networks—0
Deep Classification of Epileptic Signals—0
Data-driven forecasting of solar irradiance—0
Multivariate Bayesian Structural Time Series Model—0
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection—0
Assessing the effect of advertising expenditures upon sales: a Bayesian structural time series model—0
HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection—0
Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed PredictionCode0
Dilated Convolutional Neural Networks for Time Series ForecastingCode0
Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks—0
Modifying memories in a Recurrent Neural Network Unit—0
Neighbor-encoder—0
Benefits of Depth for Long-Term Memory of Recurrent Networks—0
Learning temporal evolution of probability distribution with Recurrent Neural Network—0
Jiffy: A Convolutional Approach to Learning Time Series Similarity—0
Convolutional Sequence Modeling Revisited—0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection—0
Relational Multi-Instance Learning for Concept Annotation from Medical Time Series—0
Initialization matters: Orthogonal Predictive State Recurrent Neural Networks—0
Recurrent Auto-Encoder Model for Multidimensional Time Series Representation—0
A Unified Method for First and Third Person Action Recognition—0
Recent Advances in Recurrent Neural Networks—0
Estimation and Inference on Heterogeneous Treatment Effects in High-Dimensional Dynamic Panels under Weak Dependence—0
A Composite Quantile Fourier Neural Network for Multi-Step Probabilistic Forecasting of Nonstationary Univariate Time Series—0
Co-Morbidity Exploration on Wearables Activity Data Using Unsupervised Pre-training and Multi-Task Learning—0
An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework—0
Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series—0
An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic ControlsCode0
Online Forecasting Matrix Factorization—0
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity—0
Towards dense object tracking in a 2D honeybee hiveCode0
Dropout Feature Ranking for Deep Learning ModelsCode0
Multi-dimensional Graph Fourier Transform—0
Multi-task learning of time series and its application to the travel demand—0
Estimating activity cycles with probabilistic methods I. Bayesian Generalised Lomb-Scargle Periodogram with TrendCode0
Estimating activity cycles with probabilistic methods II. The Mount Wilson Ca H&K data—0
Contemporary machine learning: a guide for practitioners in the physical sciences—0
Lost in Time: Temporal Analytics for Long-Term Video Surveillance—0
Multi-shot Pedestrian Re-identification via Sequential Decision Making—0
A Shapelet Transform for Multivariate Time Series Classification—0
Squeezed Convolutional Variational AutoEncoder for Unsupervised Anomaly Detection in Edge Device Industrial Internet of Things—0
Accurate Inference for Adaptive Linear ModelsCode0
Dynamic Weight Alignment for Temporal Convolutional Neural Networks—0
Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions—0
Experimental design trade-offs for gene regulatory network inference: an in silico study of the yeast Saccharomyces cerevisiae cell cycle—0
Spatial-temporal wind field prediction by Artificial Neural Networks—0
Causal Patterns: Extraction of multiple causal relationships by Mixture of Probabilistic Partial Canonical Correlation AnalysisCode0
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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