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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 101–150 of 6748 papers

TitleStatusHype
Probabilistic Time Series Forecasting with Implicit Quantile NetworksCode2
Closed-form Continuous-time Neural ModelsCode2
Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series ForecastingCode2
AST: Audio Spectrogram TransformerCode2
Neural SDEs as Infinite-Dimensional GANsCode2
Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series ForecastingCode2
Conformal prediction interval for dynamic time-seriesCode2
Deep learning for time series classificationCode2
TODS: An Automated Time Series Outlier Detection SystemCode2
TadGAN: Time Series Anomaly Detection Using Generative Adversarial NetworksCode2
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic ForecastingCode2
Liquid Time-constant NetworksCode2
Deep Learning for Time Series Forecasting: Tutorial and Literature SurveyCode2
TSFEL: Time Series Feature Extraction LibraryCode2
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing FlowsCode2
AR-Net: A simple Auto-Regressive Neural Network for time-seriesCode2
Bayesian Temporal Factorization for Multidimensional Time Series PredictionCode2
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series ForecastingCode2
Deep learning for time series classification: a reviewCode2
Optimal Transport for structured data with application on graphsCode2
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent NetworksCode2
LSTM-based Encoder-Decoder for Multi-sensor Anomaly DetectionCode2
TimePoint: Accelerated Time Series Alignment via Self-Supervised Keypoint and Descriptor LearningCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
ReFocus: Reinforcing Mid-Frequency and Key-Frequency Modeling for Multivariate Time Series ForecastingCode1
Positional Encoding in Transformer-Based Time Series Models: A SurveyCode1
Time Series Embedding Methods for Classification Tasks: A ReviewCode1
Using matrix-product states for time-series machine learningCode1
TimeCHEAT: A Channel Harmony Strategy for Irregularly Sampled Multivariate Time Series AnalysisCode1
Federated Foundation Models on Heterogeneous Time SeriesCode1
Peri-midFormer: Periodic Pyramid Transformer for Time Series AnalysisCode1
Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series ClassificationCode1
Reconstructing dynamics from sparse observations with no training on target systemCode1
Towards Generalisable Time Series Understanding Across DomainsCode1
Toward Physics-guided Time Series EmbeddingCode1
Neural Fourier Modelling: A Highly Compact Approach to Time-Series AnalysisCode1
Can LLMs Understand Time Series Anomalies?Code1
Time Series Analysis for Education: Methods, Applications, and Future DirectionsCode1
ADformer: A Multi-Granularity Transformer for EEG-Based Alzheimer's Disease AssessmentCode1
Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series AnalysisCode1
SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time SeriesCode1
ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical ImagesCode1
Time Series Representation ModelsCode1
Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series AnalysisCode1
Self-Supervised Learning for Time Series: Contrastive or Generative?Code1
MTSA-SNN: A Multi-modal Time Series Analysis Model Based on Spiking Neural NetworkCode1
PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly DetectionCode1
DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series AnalysisCode1
Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series ClassificationCode1
Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter ViewCode1
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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