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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 5801–5850 of 6748 papers

TitleStatusHype
Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition—0
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality DiscoveryCode0
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data—0
Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding—0
Self-Similarity Based Time Warping—0
A Graph Signal Processing Approach For Real-Time Traffic Prediction In Transportation Networks—0
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks—0
Bidirectional deep-readout echo state networksCode0
Beyond Sparsity: Tree Regularization of Deep Models for InterpretabilityCode0
A Correlation Based Feature Representation for First-Person Activity RecognitionCode0
Learning to Predict with Highly Granular Temporal Data: Estimating individual behavioral profiles with smart meter data—0
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features—0
Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing Framework—0
Sparsification of the Alignment Path Search Space in Dynamic Time Warping—0
Aggregated Wasserstein Metric and State Registration for Hidden Markov Models—0
Attend and Diagnose: Clinical Time Series Analysis using Attention Models—0
Interpretable Vector AutoRegressions with Exogenous Time Series—0
Long-Term Online Smoothing Prediction Using Expert Advice—0
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data—0
DeepRain: ConvLSTM Network for Precipitation Prediction using Multichannel Radar Data—0
Computer activity learning from system call time series—0
Searching for Biophysically Realistic Parameters for Dynamic Neuron Models by Genetic Algorithms from Calcium Imaging Recording—0
Channel masking for multivariate time series shapelets—0
Long-term Forecasting using Higher Order Tensor RNNsCode0
Using the quantization error from Self-Organized Map (SOM) output for detecting critical variability in large bodies of image time series in less than a minute—0
It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal]—0
Elucidation of time-dependent systems biology cell response patterns with time course network enrichment—0
Transform-Invariant Non-Parametric Clustering of Covariance Matrices and its Application to Unsupervised Joint Segmentation and Action DiscoveryCode0
Fine-grained Pattern Matching Over Streaming Time Series—0
Segment Parameter Labelling in MCMC Mean-Shift Change Detection—0
Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks—0
Calibration of Machine Learning Classifiers for Probability of Default Modelling—0
Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analytics—0
Exact Mean Computation in Dynamic Time Warping Spaces—0
A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series—0
Benchmark of Deep Learning Models on Large Healthcare MIMIC DatasetsCode0
Learning compressed representations of blood samples time series with missing dataCode0
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time SeriesCode0
Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data AnalysisCode0
The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications—0
Deep Gaussian Covariance Network—0
Time Series Prediction : Predicting Stock Price—0
Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model—0
Potential Conditional Mutual Information: Estimators, Properties and Applications—0
Deep Learning in Multiple Multistep Time Series Prediction—0
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning—0
Forecasting Across Time Series Databases using Recurrent Neural Networks on Groups of Similar Series: A Clustering ApproachCode0
Neuro Fuzzy Modelling for Prediction of Consumer Price Index—0
Bayesian Alignments of Warped Multi-Output Gaussian Processes—0
Solving differential equations with unknown constitutive relations as recurrent neural networks—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