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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 2251–2300 of 6748 papers

TitleStatusHype
Data-Folding and Hyperspace Coding for Multi-Dimensonal Time-Series Data Imaging—0
A Review of Open Source Software Tools for Time Series Analysis—0
TiSAT: Time Series Anomaly TransformerCode0
Defending Black-box Skeleton-based Human Activity ClassifiersCode0
Geometric Optimisation on Manifolds with Applications to Deep Learning—0
Monitoring Time Series With Missing Values: a Deep Probabilistic Approach—0
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data—0
Contrastive Conditional Neural Processes—0
Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees—0
Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series—0
CaSS: A Channel-aware Self-supervised Representation Learning Framework for Multivariate Time Series Classification—0
On Robust Inference in Time Series Regression—0
Exploring Physical-Based Constraints in Short-Term Load Forecasting: A Defense Mechanism Against Cyberattack—0
Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets—0
Multivariate Time Series Forecasting with Latent Graph Inference—0
Automated Few-Shot Time Series Forecasting based on Bi-level Programming—0
Provably Accurate and Scalable Linear Classifiers in Hyperbolic SpacesCode0
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion—0
KPF-AE-LSTM: A Deep Probabilistic Model for Net-Load Forecasting in High Solar Scenarios—0
Deep Sequence Modeling for Pressure Controlled Mechanical VentilationCode0
Bayesian Spillover Graphs for Dynamic NetworksCode0
Deep Q-network using reservoir computing with multi-layered readout—0
Interpretable Latent Variables in Deep State Space Models—0
Comparison of LSTM autoencoder based deep learning enabled Bayesian inference using two time series reconstruction approaches—0
Early Time-Series Classification Algorithms: An Empirical Comparison—0
Calculation of Sub-bands 1,2,5,6 for 64-Point Complex FFT and Its extension to N (=2^N) Point FFT—0
Parallel Spatio-Temporal Attention-Based TCN for Multivariate Time Series Prediction—0
Predicting the temporal dynamics of turbulent channels through deep learning—0
Boosted Ensemble Learning based on Randomized NNs for Time Series Forecasting—0
Uncovering the dynamic effects of DEX treatment on lung cancer by integrating bioinformatic inference and multiscale modeling of scRNA-seq and proteomics data—0
Molecular Dynamics of Polymer-lipids in Solution from Supervised Machine Learning—0
FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers—0
Path sampling of recurrent neural networks by incorporating known physics—0
Taming the Long Tail of Deep Probabilistic Forecasting—0
Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory PredictionCode0
Hierarchical Linear Dynamical System for Representing Notes from Recorded Audio—0
Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets—0
ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting—0
Regularized Bilinear Discriminant Analysis for Multivariate Time Series Data—0
Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costsCode0
Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection—0
Mental State Classification Using Multi-graph Features—0
Capturing Actionable Dynamics with Structured Latent Ordinary Differential EquationsCode0
Novel techniques for improving NNetEn entropy calculation for short and noisy time series—0
Long-Term Missing Value Imputation for Time Series Data Using Deep Neural Networks—0
Sequential asset ranking in nonstationary time series—0
Predicting the impact of treatments over time with uncertainty aware neural differential equationsCode0
NeuroView-RNN: It's About Time—0
Deep Recurrent Modelling of Granger Causality with Latent Confounding—0
Simulating Network Paths with Recurrent Buffering Units—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