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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 601–650 of 6748 papers

TitleStatusHype
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series dataCode1
Monte Carlo Simulation of SDEs using GANsCode1
Stock price prediction using Generative Adversarial NetworksCode1
Neural Transformation Learning for Deep Anomaly Detection Beyond ImagesCode1
Stiff Neural Ordinary Differential EquationsCode1
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
Gated Transformer Networks for Multivariate Time Series ClassificationCode1
Evaluation of deep learning models for multi-step ahead time series predictionCode1
Active multi-fidelity Bayesian online changepoint detectionCode1
Neural ODE ProcessesCode1
An Experimental Review on Deep Learning Architectures for Time Series ForecastingCode1
An Empirical Framework for Domain Generalization in Clinical SettingsCode1
Learning Discriminative Prototypes with Dynamic Time WarpingCode1
Temporal Cluster Matching for Change Detection of Structures from Satellite ImageryCode1
Hierarchical forecasting with a top-down alignment of independent level forecastsCode1
Spectral Temporal Graph Neural Network for Multivariate Time-series ForecastingCode1
Affect2MM: Affective Analysis of Multimedia Content Using Emotion CausalityCode1
UnICORNN: A recurrent model for learning very long time dependenciesCode1
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial AttackCode1
PyRCN: A Toolbox for Exploration and Application of Reservoir Computing NetworksCode1
Scalable Learning With a Structural Recurrent Neural Network for Short-Term Traffic PredictionCode1
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time SeriesCode1
Missing Value Imputation on Multidimensional Time SeriesCode1
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time SeriesCode1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdownCode1
Crop mapping from image time series: deep learning with multi-scale label hierarchiesCode1
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural NetworksCode1
Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and OutliersCode1
Network of Tensor Time SeriesCode1
Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time SeriesCode1
Domain Adaptation for Time Series Forecasting via Attention SharingCode1
Early Abandoning and Pruning for Elastic Distances including Dynamic Time WarpingCode1
Last Query Transformer RNN for knowledge tracingCode1
NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series ForecastingCode1
MALI: A memory efficient and reverse accurate integrator for Neural ODEsCode1
FedMood: Federated Learning on Mobile Health Data for Mood DetectionCode1
Deep Semi-Supervised Learning for Time Series ClassificationCode1
NRTSI: Non-Recurrent Time Series ImputationCode1
CKConv: Continuous Kernel Convolution For Sequential DataCode1
Uncertain Time Series Classification With Shapelet TransformCode1
MultiRocket: Multiple pooling operators and transformations for fast and effective time series classificationCode1
Adjusting for Autocorrelated Errors in Neural Networks for Time SeriesCode1
A Review of Graph Neural Networks and Their Applications in Power SystemsCode1
Multi-Time Attention Networks for Irregularly Sampled Time SeriesCode1
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
Bayesian hierarchical stacking: Some models are (somewhere) usefulCode1
Interpretable Models for Granger Causality Using Self-explaining Neural NetworksCode1
Discrete Graph Structure Learning for Forecasting Multiple Time SeriesCode1
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro DataCode1
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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