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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 6301–6350 of 6748 papers

TitleStatusHype
DROCC: Deep Robust One-Class Classification—0
DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift—0
DSTP-RNN: a dual-stage two-phase attention-based recurrent neural networks for long-term and multivariate time series prediction—0
DTWSSE: Data Augmentation with a Siamese Encoder for Time Series—0
Contrastive Blind Denoising Autoencoder for Real-Time Denoising of Industrial IoT Sensor Data—0
Dual-Forecaster: A Multimodal Time Series Model Integrating Descriptive and Predictive Texts—0
Dual reparametrized Variational Generative Model for Time-Series Forecasting—0
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn—0
Duration and Interval Hidden Markov Model for Sequential Data Analysis—0
DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting—0
Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies—0
Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series—0
Dynamical prediction of two meteorological factors using the deep neural network and the long short term memory (1)—0
Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (2)—0
Dynamical spectral unmixing of multitemporal hyperspectral images—0
Dynamical Systems as Temporal Feature Spaces—0
Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment—0
Dynamic and Static Topic Model for Analyzing Time-Series Document Collections—0
Dynamic and Stochastic Rational Behavior—0
Dynamic and Thermodynamic Models of Adaptation—0
Dynamic Asymmetric Causality Tests with an Application—0
Hidden Markov Neural Networks—0
Dynamic Boltzmann Machines for Second Order Moments and Generalized Gaussian Distributions—0
Dynamic Clustering in Federated Learning—0
Dynamic clustering of time series data—0
Dynamic Combination of Heterogeneous Models for Hierarchical Time Series—0
Dynamic Covariance Models for Multivariate Financial Time Series—0
Dynamic Deep Convolutional Candlestick Learner—0
Dynamic-Depth Context Tree Weighting—0
Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces—0
Dynamic Graph Learning based on Graph Laplacian—0
Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions—0
Dynamic Hurst Exponent in Time Series—0
Dynamic imaging using a deep generative SToRM (Gen-SToRM) model—0
Dynamic imaging using Motion-Compensated SmooThness Regularization on Manifolds (MoCo-SToRM)—0
Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)—0
Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems—0
Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction—0
Enhancing Digital Health Services: A Machine Learning Approach to Personalized Exercise Goal Setting—0
Dynamic Prediction Length for Time Series with Sequence to Sequence Networks—0
Dynamic Prediction Model for NOx Emission of SCR System Based on Hybrid Data-driven Algorithms—0
Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation—0
Dynamic Principal Component Analysis: Identifying the Relationship between Multiple Air Pollutants—0
Dynamic Probabilistic Network Based Human Action Recognition—0
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting—0
Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19—0
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions—0
Dynamics and triggers of misinformation on vaccines—0
Dynamic Spatiotemporal Graph Neural Network with Tensor Network—0
Dynamic structure of stock communities: A comparative study between stock returns and turnover rates—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