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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 851–875 of 6748 papers

TitleStatusHype
Uncertainty-DTW for Time Series and SequencesCode0
Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolution—0
Monitoring the Dynamic Networks of Stock Returns—0
Multimodal Estimation of Change Points of Physiological Arousal in DriversCode0
A Novel Sparse Bayesian Learning and Its Application to Fault Diagnosis for Multistation Assembly Systems—0
ViT-CAT: Parallel Vision Transformers with Cross Attention Fusion for Popularity Prediction in MEC Networks—0
Adaptive Estimation of Graphical Models under Total Positivity—0
Forecasting Graph Signals with Recursive MIMO Graph Filters—0
Cap or No Cap? What Can Governments Do to Promote EV Sales?—0
Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots—0
TILDE-Q: A Transformation Invariant Loss Function for Time-Series ForecastingCode1
Sinusoidal Frequency Estimation by Gradient DescentCode1
RapidAI4EO: Mono- and Multi-temporal Deep Learning models for Updating the CORINE Land Cover Product—0
Deep Subspace Encoders for Nonlinear System Identification—0
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise—0
Modelling the Bitcoin prices and the media attention to Bitcoin via the jump-type processes—0
Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data—0
WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting—0
Preparing fMRI Data for Statistical Analysis—0
Novelty Detection in Time Series via Weak Innovations Representation: A Deep Learning Approach—0
Exploring Self-Attention for Crop-type Classification Explainability—0
Applications of Machine Learning in Pharmacogenomics: Clustering Plasma Concentration-Time Curves—0
Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble—0
FingerFlex: Inferring Finger Trajectories from ECoG signalsCode1
SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing DataCode1
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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