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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
LibCity: An Open Library for Traffic PredictionCode2
LogAI: A Library for Log Analytics and IntelligenceCode2
MOMENT: A Family of Open Time-series Foundation ModelsCode2
Deep Learning for Time Series Forecasting: Tutorial and Literature SurveyCode2
HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in PythonCode2
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and OutlookCode2
FITS: Modeling Time Series with 10k ParametersCode2
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series ForecastingCode2
TSFEL: Time Series Feature Extraction LibraryCode2
Generative Time Series Forecasting with Diffusion, Denoise, and DisentanglementCode2
JANA: Jointly Amortized Neural Approximation of Complex Bayesian ModelsCode2
MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series AnalysisCode2
Diffusion-based Time Series Imputation and Forecasting with Structured State Space ModelsCode2
Learning Deep Time-index Models for Time Series ForecastingCode2
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based ReconciliationCode2
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic ForecastingCode2
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series ForecastingCode2
Conformal prediction interval for dynamic time-seriesCode2
Deep learning for time series classification: a reviewCode2
Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series ForecastingCode2
NeuralProphet: Explainable Forecasting at ScaleCode2
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and ProspectsCode2
Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series AnalysisCode1
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetCode1
Closed-Form Diffeomorphic Transformations for Time Series AlignmentCode1
A biologically plausible neural network for Slow Feature AnalysisCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Color-aware two-branch DCNN for efficient plant disease classificationCode1
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
A bio-inspired bistable recurrent cell allows for long-lasting memoryCode1
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task LearningCode1
Classification of Long Sequential Data using Circular Dilated Convolutional Neural NetworksCode1
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image RepresentationCode1
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural NetworksCode1
CKConv: Continuous Kernel Convolution For Sequential DataCode1
A Comprehensive Survey of Regression Based Loss Functions for Time Series ForecastingCode1
Data-driven discovery of intrinsic dynamicsCode1
ClaSP -- Parameter-free Time Series SegmentationCode1
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series DataCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
Changing Fashion CulturesCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
A Bayesian neural network predicts the dissolution of compact planetary systemsCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
ClaSP - Time Series SegmentationCode1
The Signature Kernel is the solution of a Goursat PDECode1
Can LLMs Understand Time Series Anomalies?Code1
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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