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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 851–900 of 6748 papers

TitleStatusHype
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series ForecastingCode1
Attentive Neural Controlled Differential Equations for Time-series Classification and ForecastingCode1
An efficient aggregation method for the symbolic representation of temporal dataCode1
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data DetectionCode1
Improving Clinical Outcome Predictions Using Convolution over Medical Entities with Multimodal LearningCode1
Few-Shot Forecasting of Time-Series with Heterogeneous ChannelsCode1
Finding active galactic nuclei through FinkCode1
Automatic Posterior Transformation for Likelihood-Free InferenceCode1
Few-Shot One-Class Classification via Meta-LearningCode1
Financial Time Series Data Processing for Machine LearningCode1
TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series AnalysisCode1
Improving Deep Learning Interpretability by Saliency Guided TrainingCode1
Inductive Graph Neural Networks for Spatiotemporal KrigingCode1
Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from DataCode1
First De-Trend then Attend: Rethinking Attention for Time-Series ForecastingCode1
An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life predictionCode1
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesCode1
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingCode1
Time Series Anomaly Detection by Cumulative Radon FeaturesCode1
Forecasting in Non-stationary Environments with Fuzzy Time SeriesCode1
Time series forecasting with Gaussian Processes needs priorsCode1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
Time Series Change Point Detection with Self-Supervised Contrastive Predictive CodingCode1
Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering AlgorithmCode1
An Empirical Evaluation of Time-Series Feature SetsCode1
Advancing the State-of-the-Art for ECG Analysis through Structured State Space ModelsCode1
Time Series Embedding Methods for Classification Tasks: A ReviewCode1
An Empirical Framework for Domain Generalization in Clinical SettingsCode1
Forecasting Sequential Data using Consistent Koopman AutoencodersCode1
Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series ClassificationCode1
Hierarchical forecasting with a top-down alignment of independent level forecastsCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Time Series Representation ModelsCode1
Forecasting with Deep LearningCode1
Backdoor Attacks on Time Series: A Generative ApproachCode1
Improved Online Conformal Prediction via Strongly Adaptive Online LearningCode1
Forecasting with time series imagingCode1
Informer: Beyond Efficient Transformer for Long Sequence Time-Series ForecastingCode1
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series ForecastingCode1
Evaluation of post-hoc interpretability methods in time-series classificationCode1
FrAug: Frequency Domain Augmentation for Time Series ForecastingCode1
From Fourier to Koopman: Spectral Methods for Long-term Time Series PredictionCode1
From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecastingCode1
Learning future terrorist targets through temporal meta-graphsCode1
Neural Rough Differential Equations for Long Time SeriesCode1
A Bayesian neural network predicts the dissolution of compact planetary systemsCode1
Generative adversarial networks in time series: A survey and taxonomyCode1
Self-Supervised Learning for Time Series: Contrastive or Generative?Code1
WOODS: Benchmarks for Out-of-Distribution Generalization in Time SeriesCode1
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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