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 351–400 of 6748 papers

TitleStatusHype
A Neural PDE Solver with Temporal Stencil ModelingCode1
Deep Switching Auto-Regressive Factorization:Application to Time Series ForecastingCode1
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
CKConv: Continuous Kernel Convolution For Sequential DataCode1
DeepVARwT: Deep Learning for a VAR Model with TrendCode1
DeepVATS: Deep Visual Analytics for Time SeriesCode1
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series DataCode1
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdownCode1
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
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural NetworksCode1
Diffusion-based Conditional ECG Generation with Structured State Space ModelsCode1
ClaSP -- Parameter-free Time Series SegmentationCode1
Diffusion Generative Models in Infinite DimensionsCode1
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local ExplanationsCode1
Dimensionality reduction to maximize prediction generalization capabilityCode1
Data-driven discovery of intrinsic dynamicsCode1
Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series ForecastingCode1
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic PredictionCode1
Domain Adaptation for Time-Series Classification to Mitigate Covariate ShiftCode1
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series TransformerCode1
A Multi-view Multi-task Learning Framework for Multi-variate Time Series ForecastingCode1
InceptionTime: Finding AlexNet for Time Series ClassificationCode1
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series DataCode1
Dynamic Data Augmentation with Gating Networks for Time Series RecognitionCode1
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time SeriesCode1
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking ApplicationsCode1
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
A Deep Learning Approach to Analyzing Continuous-Time SystemsCode1
Early Abandoning and Pruning for Elastic Distances including Dynamic Time WarpingCode1
ClaSP - Time Series SegmentationCode1
General Evaluation for Instruction Conditioned Navigation using Dynamic Time WarpingCode1
An Evaluation of Change Point Detection AlgorithmsCode1
Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep LearningCode1
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time seriesCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
Accelerating Recurrent Neural Networks for Gravitational Wave ExperimentsCode1
Ensemble Conformalized Quantile Regression for Probabilistic Time Series ForecastingCode1
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting EpidemicsCode1
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load ForecastingCode1
Estimation of Continuous Blood Pressure from PPG via a Federated Learning ApproachCode1
Changing Fashion CulturesCode1
Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly BenchmarkCode1
Evaluation of deep learning models for multi-step ahead time series predictionCode1
Correlation-aware Unsupervised Change-point Detection via Graph Neural NetworksCode1
Explaining Time Series Predictions with Dynamic MasksCode1
Expressing Multivariate Time Series as Graphs with Time Series Attention TransformerCode1
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series dataCode1
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic ForecastingCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
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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