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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 2901–2950 of 6748 papers

TitleStatusHype
Generating Student Feedback from Time-Series Data Using Reinforcement Learning—0
Generating Trading Signals by ML algorithms or time series ones?—0
Generation of a Supervised Classification Algorithm for Time-Series Variable Stars with an Application to the LINEAR Dataset—0
Generation of Synthetic Multi-Resolution Time Series Load Data—0
Generative adversarial network based on chaotic time series—0
Fusing Continuous-valued Medical Labels using a Bayesian Model—0
Fused Lasso for Feature Selection using Structural Information—0
Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination—0
Artificial neural network as a universal model of nonlinear dynamical systems—0
Generative Adversarial Networks in finance: an overview—0
Among-site variability in the stochastic dynamics of East African coral reefs—0
Detection and Forecasting of Extreme event in Stock Price Triggered by Fundamental, Technical, and External Factors—0
Generative Modeling of Hidden Functional Brain Networks—0
Generative modeling of spatio-temporal weather patterns with extreme event conditioning—0
Functional Time Series Forecasting: Functional Singular Spectrum Analysis Approaches—0
Conditional Risk Minimization for Stochastic Processes—0
Generative Pre-Trained Transformer for Cardiac Abnormality Detection—0
Functional Mixtures-of-Experts—0
Generative Time-series Modeling with Fourier Flows—0
Functional Connectivity Dynamics show Resting-State Instability and Rightward Parietal Dysfunction in ADHD—0
Gene Regulatory Network Inference with Latent Force Models—0
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving—0
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting—0
Kernel Hypothesis Testing with Set-valued Data—0
Geodesic Properties of a Generalized Wasserstein Embedding for Time Series Analysis—0
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds—0
Functional Classwise Principal Component Analysis: A Novel Classification Framework—0
Geometric feature performance under downsampling for EEG classification tasks—0
Functional Bayesian Filter—0
Geometric Optimisation on Manifolds with Applications to Deep Learning—0
Functional Annotation of Human Cognitive States using Graph Convolution Networks—0
GesturePod: Enabling On-device Gesture-based Interaction for White Cane Users—0
Conditional independence testing with a single realization of a multivariate nonstationary nonlinear time series—0
A Robust Score-Driven Filter for Multivariate Time Series—0
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings—0
GG-SSMs: Graph-Generating State Space Models—0
Global Constraint Catalog, Volume II, Time-Series Constraints—0
Global Flood Prediction: a Multimodal Machine Learning Approach—0
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series—0
Conditional heteroskedasticity in crypto-asset returns—0
Fully convolutional networks for structural health monitoring through multivariate time series classification—0
Conditional Generative Models for Counterfactual Explanations—0
A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow—0
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models—0
GP Kernels for Cross-Spectrum Analysis—0
From time-series transcriptomics to gene regulatory networks: a review on inference methods—0
Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution—0
GPU Computing in Bayesian Inference of Realized Stochastic Volatility Model—0
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics—0
From Time Series to Euclidean Spaces: On Spatial Transformations for Temporal Clustering—0
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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