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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 2951–3000 of 6748 papers

TitleStatusHype
A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network—0
Bayesian Neural Networks for Macroeconomic Analysis—0
A Soft Computing Approach for Selecting and Combining Spectral Bands—0
Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series—0
Granger Mediation Analysis of Multiple Time Series with an Application to fMRI—0
Graph2Seq: Scalable Learning Dynamics for Graphs—0
GraphAD: A Graph Neural Network for Entity-Wise Multivariate Time-Series Anomaly Detection—0
Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting -- Full version—0
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning—0
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals—0
Enforcing constraints for interpolation and extrapolation in Generative Adversarial Networks—0
Graph-based era segmentation of international financial integration—0
Energy time series forecasting-Analytical and empirical assessment of conventional and machine learning models—0
Graph-based Predictable Feature Analysis—0
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks—0
Graph Anomaly Detection in Time Series: A Survey—0
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—0
Graph Deep Factors for Forecasting—0
A novel health risk model based on intraday physical activity time series collected by smartphones—0
Graph Filters for Signal Processing and Machine Learning on Graphs—0
A High GOPs/Slice Time Series Classifier for Portable and Embedded Biomedical Applications—0
Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction—0
Graphical estimation of multivariate count time series—0
Graphical LASSO Based Model Selection for Time Series—0
Energy Predictive Models with Limited Data using Transfer Learning—0
Graphical RNN Models—0
Graphical Time Warping for Joint Alignment of Multiple Curves—0
Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation—0
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction—0
Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification—0
Energy-Efficient Seizure Detection Suitable for low-power Applications—0
Graph neural network-based fault diagnosis: a review—0
Causal inference for climate change events from satellite image time series using computer vision and deep learning—0
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs—0
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware—0
Graph Spectral Embedding for Parsimonious Transmission of Multivariate Time Series—0
Graph state-space models—0
Grasping Core Rules of Time Series through Pure Models—0
GraSSNet: Graph Soft Sensing Neural Networks—0
Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning—0
Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection—0
Energy consumption forecasting using a stacked nonparametric Bayesian approach—0
Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control—0
Griffon: Reasoning about Job Anomalies with Unlabeled Data in Cloud-based Platforms—0
Grouped Convolutional Neural Networks for Multivariate Time Series—0
Grouped self-attention mechanism for a memory-efficient Transformer—0
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components—0
GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER—0
A Novel Granular-Based Bi-Clustering Method of Deep Mining the Co-Expressed Genes—0
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning—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