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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 1601–1650 of 6748 papers

TitleStatusHype
Research of an optimization model for servicing a network of ATMs and information payment terminals—0
Optimal Event Monitoring through Internet Mashup over Multivariate Time Series—0
Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability—0
Universal hidden monotonic trend estimation with contrastive learning—0
tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving GraphsCode0
Flipped Classroom: Effective Teaching for Time Series ForecastingCode0
Temporal-Spatial dependencies ENhanced deep learning model (TSEN) for household leverage series forecasting—0
Modelling Emotion Dynamics in Song Lyrics with State Space Models—0
From time-series transcriptomics to gene regulatory networks: a review on inference methods—0
Extreme-Long-short Term Memory for Time-series Prediction—0
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain—0
Bandwidth-efficient distributed neural network architectures with application to body sensor networks—0
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems—0
An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different DimensionsCode0
Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing—0
Latent Temporal Flows for Multivariate Analysis of Wearables Data—0
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm—0
LEAVES: Learning Views for Time-Series Data in Contrastive Learning—0
Early Discovery of Disappearing Entities in Microblogs—0
Data augmentation on-the-fly and active learning in data stream classificationCode0
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters—0
Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing—0
Anomaly detection in dynamic networksCode0
Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patients—0
Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks—0
Combining datasets to increase the number of samples and improve model fittingCode0
Class-Specific Explainability for Deep Time Series ClassifiersCode0
Short-term prediction of stream turbidity using surrogate data and a meta-model approach—0
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware—0
Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting—0
Self-explaining Hierarchical Model for Intraoperative Time SeriesCode0
A Clustering Algorithm for Correlation Quickest Hub Discovery Mixing Time Evolution and Random Matrix Theory—0
Multi-Task Dynamical SystemsCode0
ANFIS-based prediction of power generation for combined cycle power plant—0
An Empirical Study on How the Developers Discussed about Pandas Topics—0
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts—0
Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting—0
From Rules to Regs: A Structural Topic Model of Collusion Research—0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural NetworksCode0
Interpreting County Level COVID-19 Infection and Feature Sensitivity using Deep Learning Time Series ModelsCode0
Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting—0
Biological neurons act as generalization filters in reservoir computing—0
Learning Video-independent Eye Contact Segmentation from In-the-Wild VideosCode0
Tripletformer for Probabilistic Interpolation of Irregularly sampled Time SeriesCode0
Feature Importance for Time Series Data: Improving KernelSHAP—0
The Local to Unity Dynamic Tobit Model—0
Efficient probabilistic reconciliation of forecasts for real-valued and count time series—0
Stock Volatility Prediction using Time Series and Deep Learning Approach—0
GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks—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