SOTAVerified

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 201–250 of 6748 papers

TitleStatusHype
Counterfactual Explanations for Machine Learning on Multivariate Time Series DataCode1
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact DataCode1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
A Novel Deep Learning Model for Hotel Demand and Revenue Prediction amid COVID-19Code1
Deep and Confident Prediction for Time Series at UberCode1
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series ForecastingCode1
Deep Dynamic Factor ModelsCode1
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series DataCode1
Adjusting for Autocorrelated Errors in Neural Networks for Time SeriesCode1
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome PredictionCode1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality ModelingCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential EquationsCode1
Self-Supervised Time Series Representation Learning via Cross Reconstruction TransformerCode1
A Synthetic Texas Power System with Time-Series Weather-Dependent Spatiotemporal ProfilesCode1
Advancing the State-of-the-Art for ECG Analysis through Structured State Space ModelsCode1
Cost-effective Interactive Attention Learning with Neural Attention ProcessesCode1
Deep Learning for Time Series Classification and Extrinsic Regression: A Current SurveyCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
A Time-dependent SIR model for COVID-19 with Undetectable Infected PersonsCode1
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent AutoencodersCode1
Adversarial Attacks on Time SeriesCode1
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulationsCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal BootstrappingCode1
Adversarial Examples in Deep Learning for Multivariate Time Series RegressionCode1
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series DataCode1
Deep Stock PredictionsCode1
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
COT-GAN: Generating Sequential Data via Causal Optimal TransportCode1
Random Dilated Shapelet Transform: A New Approach for Time Series ShapeletsCode1
A Comprehensive Survey of Regression Based Loss Functions for Time Series ForecastingCode1
Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering AlgorithmCode1
Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence CaseCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
DeepVATS: Deep Visual Analytics for Time SeriesCode1
Convolution-enhanced Evolving Attention NetworksCode1
Automatic Change-Point Detection in Time Series via Deep LearningCode1
Detection of gravitational-wave signals from binary neutron star mergers using machine learningCode1
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
Contrastive Neural Processes for Self-Supervised LearningCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
Affect2MM: Affective Analysis of Multimedia Content Using Emotion CausalityCode1
Time series forecasting with Gaussian Processes needs priorsCode1
A biologically plausible neural network for Slow Feature AnalysisCode1
Diffusion Generative Models in Infinite DimensionsCode1
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local ExplanationsCode1
Convolutional Radio Modulation Recognition NetworksCode1
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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