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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 701–750 of 6748 papers

TitleStatusHype
Deep Contrastive One-Class Time Series Anomaly DetectionCode1
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
Deep Explicit Duration Switching Models for Time SeriesCode1
Deep Dynamic Factor ModelsCode1
A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingCode1
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series DataCode1
Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly DetectionCode1
Graph Neural Networks for Multivariate Time Series Regression with Application to Seismic DataCode1
NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series ForecastingCode1
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological OrderCode1
Deep Latent State Space Models for Time-Series GenerationCode1
Neural Additive Vector Autoregression Models for Causal Discovery in Time SeriesCode1
Deep Isolation Forest for Anomaly DetectionCode1
Neural Rough Differential Equations for Long Time SeriesCode1
Forecasting in Non-stationary Environments with Fuzzy Time SeriesCode1
Neural Controlled Differential Equations for Irregular Time SeriesCode1
Deep Learning-based Damage Mapping with InSAR Coherence Time SeriesCode1
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning RulesCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
Deep Learning for Time Series Classification and Extrinsic Regression: A Current SurveyCode1
Active multi-fidelity Bayesian online changepoint detectionCode1
Deep reconstruction of strange attractors from time seriesCode1
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood RiskCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
Hierarchical forecasting with a top-down alignment of independent level forecastsCode1
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro DataCode1
Finding active galactic nuclei through FinkCode1
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease ProgressionCode1
Deep Semi-Supervised Learning for Time Series ClassificationCode1
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data ProcessingCode1
Finding Scientific Topics in Continuously Growing Text CorporaCode1
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
Diffusion-based Conditional ECG Generation with Structured State Space ModelsCode1
Nonlinear proper orthogonal decomposition for convection-dominated flowsCode1
Financial Time Series Data Processing for Machine LearningCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic PredictionCode1
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdownCode1
A Multi-view Multi-task Learning Framework for Multi-variate Time Series ForecastingCode1
NTS-NOTEARS: Learning Nonparametric DBNs With Prior KnowledgeCode1
Financial time series forecasting with multi-modality graph neural networkCode1
On Contrastive Representations of Stochastic ProcessesCode1
DEPTS: Deep Expansion Learning for Periodic Time Series ForecastingCode1
FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable StocksCode1
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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