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 726–750 of 6748 papers

TitleStatusHype
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