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 5751–5800 of 6748 papers

TitleStatusHype
Temporal Stability in Predictive Process MonitoringCode0
Long-Range Correlation Underlying Childhood Language and Generative Models—0
Comparative analysis of criteria for filtering time series of word usage frequencies—0
RNN-based counterfactual prediction, with an application to homestead policy and public schoolingCode0
Temporal Attention augmented Bilinear Network for Financial Time-Series Data AnalysisCode0
Learning User Intent from Action Sequences on Interactive Systems—0
TrueSkill Through Time: Revisiting the History of ChessCode0
Preventing Gradient Explosions in Gated Recurrent Units—0
Subset Selection and Summarization in Sequential Data—0
Random Projection Filter Bank for Time Series Data—0
Practical Data-Dependent Metric Compression with Provable Guarantees—0
Discriminative State Space Models—0
Dynamic-Depth Context Tree Weighting—0
YASS: Yet Another Spike SorterCode0
Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories—0
A Neural Stochastic Volatility Model—0
Multivariate Time Series Classification with WEASEL+MUSECode0
A Multi-Horizon Quantile Recurrent ForecasterCode0
Role of Deep LSTM Neural Networks And WiFi Networks in Support of Occupancy Prediction in Smart Buildings—0
OSTSC: Over Sampling for Time Series Classification in R—0
Forecasting of a Hierarchical Functional Time Series on Example of Macromodel for Day and Night Air Pollution in Silesia Region: A Critical Overview—0
Warped-Linear Models for Time Series Classification—0
Random matrix approach to estimation of high-dimensional factor models—0
Variational Encoding of Complex DynamicsCode0
Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition—0
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality DiscoveryCode0
An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series—0
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
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features—0
Learning to Predict with Highly Granular Temporal Data: Estimating individual behavioral profiles with smart meter data—0
A Correlation Based Feature Representation for First-Person Activity RecognitionCode0
Sparsification of the Alignment Path Search Space in Dynamic Time Warping—0
Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing Framework—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
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data—0
Long-Term Online Smoothing Prediction Using Expert Advice—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
Learning Linear Dynamical Systems via Spectral FilteringCode1
Channel masking for multivariate time series shapelets—0
hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature ExtractionCode1
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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