SOTAVerified

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 6351–6400 of 6748 papers

TitleStatusHype
A Novel Time-Varying Spectral Filtering Algorithm for Reconstruction of Motion Artifact Corrupted Heart Rate Signals During Intense Physical Activities Using a Wearable Photoplethysmogram Sensor—0
Multivariate Time Series Classification Using Dynamic Time Warping Template Selection for Human Activity Recognition—0
Regularized Estimation of Piecewise Constant Gaussian Graphical Models: The Group-Fused Graphical Lasso—0
Multiclass Classification of Cervical Cancer Tissues by Hidden Markov Model—0
Probabilistic Programming with Gaussian Process Memoization—0
Time-dependent scaling patterns in high frequency financial data—0
Anomalous volatility scaling in high frequency financial data—0
Distilling Knowledge from Deep Networks with Applications to Healthcare Domain—0
Moving poselets: A discriminative and interpretable skeletal motion representation for action recognition—0
Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations—0
Learning Theory and Algorithms for Forecasting Non-stationary Time Series—0
Minimax Time Series Prediction—0
Rate-Agnostic (Causal) Structure Learning—0
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels—0
Temporal Subspace Clustering for Human Motion Segmentation—0
GP Kernels for Cross-Spectrum Analysis—0
Sequential visibility-graph motifs—0
Bidirectional Recurrent Neural Networks as Generative Models—0
Recognizing Temporal Linguistic Expression Pattern of Individual with Suicide Risk on Social Media—0
The Automatic Statistician: A Relational Perspective—0
Black box variational inference for state space modelsCode0
Learning Representations from EEG with Deep Recurrent-Convolutional Neural NetworksCode0
Learning Representations Using Complex-Valued Nets—0
Probabilistic Segmentation via Total Variation Regularization—0
Deep Kalman FiltersCode0
A genetic algorithm to discover flexible motifs with supportCode0
Graph-based denoising for time-varying point clouds—0
Seeing the Unseen Network: Inferring Hidden Social Ties from Respondent-Driven Sampling—0
Signal Fluctuation Sensitivity: an improved metric for optimizing detection of resting-state fMRI networks—0
Learning to Diagnose with LSTM Recurrent Neural Networks—0
Instantaneous Modelling and Reverse Engineering of DataConsistent Prime Models in Seconds!—0
Granger Causality in Multi-variate Time Series using a Time Ordered Restricted Vector Autoregressive Model—0
A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property—0
Population size predicts lexical diversity, but so does the mean sea level - why it is important to correctly account for the structure of temporal data—0
Prediction of Dynamical time Series Using Kernel Based Regression and Smooth Splines—0
Linear-time Detection of Non-linear Changes in Massively High Dimensional Time Series—0
Blitzkriging: Kronecker-structured Stochastic Gaussian Processes—0
Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks—0
Uncovering the evolution of non-stationary stochastic variables: the example of asset volume-price fluctuations—0
Data-driven detrending of nonstationary fractal time series with echo state networksCode0
Accelerometer based Activity Classification with Variational Inference on Sticky HDP-SLDS—0
Clustering Noisy Signals with Structured Sparsity Using Time-Frequency RepresentationCode0
Multifractal Flexibly Detrended Fluctuation Analysis—0
Optimizing and Contrasting Recurrent Neural Network Architectures—0
A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas—0
Quantification in-the-wild: data-sets and baselines—0
Dynamical spectral unmixing of multitemporal hyperspectral images—0
Detecting a trend change in cross-border epidemic transmission—0
Is the Indian Stock Market efficient - A comprehensive study of Bombay Stock Exchange Indices—0
p-Markov Gaussian Processes for Scalable and Expressive Online Bayesian Nonparametric Time Series Forecasting—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