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 701–750 of 6748 papers

TitleStatusHype
Learning Fast and Slow for Online Time Series ForecastingCode1
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural NetworksCode1
Learning the dynamics of technical trading strategiesCode1
Learning the spatio-temporal relationship between wind and significant wave height using deep learningCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural NetworksCode1
LightCTS: A Lightweight Framework for Correlated Time Series ForecastingCode1
Light curve completion and forecasting using fast and scalable Gaussian processes (MuyGPs)Code1
Active multi-fidelity Bayesian online changepoint detectionCode1
catch22: CAnonical Time-series CHaracteristicsCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
Local Evaluation of Time Series Anomaly Detection AlgorithmsCode1
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series DataCode1
Long Expressive Memory for Sequence ModelingCode1
Long-Range Transformers for Dynamic Spatiotemporal ForecastingCode1
Long Short-Term Memory Spiking Networks and Their ApplicationsCode1
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural NetworksCode1
Machine Learning-Based Unbalance Detection of a Rotating Shaft Using Vibration DataCode1
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
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
MALI: A memory efficient and reverse accurate integrator for Neural ODEsCode1
Manifold Topology Divergence: a Framework for Comparing Data ManifoldsCode1
Manifold Topology Divergence: a Framework for Comparing Data Manifolds.Code1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market OpportunitiesCode1
Calibration of Google Trends Time SeriesCode1
Merlion: A Machine Learning Library for Time SeriesCode1
Meta-learning framework with applications to zero-shot time-series forecastingCode1
Leveraging Class Hierarchies with Metric-Guided Prototype LearningCode1
MFRFNN: Multi-Functional Recurrent Fuzzy Neural Network for Chaotic Time Series PredictionCode1
A Multi-Scale Decomposition MLP-Mixer for Time Series AnalysisCode1
Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for HealthcareCode1
MODALS: Modality-agnostic Automated Data Augmentation in the Latent SpaceCode1
Modeling Continuous Stochastic Processes with Dynamic Normalizing FlowsCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic PredictionCode1
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval PredictorsCode1
Motiflets -- Simple and Accurate Detection of Motifs in Time SeriesCode1
A Multi-view Multi-task Learning Framework for Multi-variate Time Series ForecastingCode1
MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activityCode1
CAMul: Calibrated and Accurate Multi-view Time-Series ForecastingCode1
MTSA-SNN: A Multi-modal Time Series Analysis Model Based on Spiking Neural NetworkCode1
MTSS-GAN: Multivariate Time Series Simulation Generative Adversarial NetworksCode1
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
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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