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 4701–4750 of 6748 papers

TitleStatusHype
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula ProcessesCode0
Patterns of Urban Foot Traffic Dynamics—0
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector—0
On the Duality between Network Flows and Network Lasso—0
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion—0
DPSOM: Deep Probabilistic Clustering with Self-Organizing MapsCode0
The Neural Moving Average Model for Scalable Variational Inference of State Space ModelsCode0
Stationarity of the detrended price return in stock markets—0
End-to-end learning of energy-based representations for irregularly-sampled signals and imagesCode0
MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive—0
Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels—0
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open QuestionsCode0
A Video Recognition Method by using Adaptive Structural Learning of Long Short Term Memory based Deep Belief Network—0
A data imputation method for multivariate time series based on generative adversarial network—0
Normalisation of Weights and Firing Rates in Spiking Neural Networks with Spike-Timing-Dependent Plasticity—0
ATOL: Measure Vectorization for Automatic Topologically-Oriented Learning—0
Machine Learning vs Statistical Methods for Time Series Forecasting: Size MattersCode0
Stochastic Weight Matrix-based Regularization Methods for Deep Neural NetworksCode0
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison—0
Set Functions for Time SeriesCode0
The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks—0
InfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers—0
Learning Through Limited Self-Supervision: Improving Time-Series Classification Without Additional Data via Auxiliary Tasks—0
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction—0
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS—0
Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps—0
Hierarchical Probabilistic Model for Blind Source Separation via Legendre TransformationCode0
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep NetworksCode0
MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS—0
Continuous Convolutional Neural Network forNonuniform Time Series—0
Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series—0
Actor-Critic Approach for Temporal Predictive Clustering—0
RISE and DISE: Two Frameworks for Learning from Time Series with Missing Data—0
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario—0
Recurrent Neural Networks are Universal Filters—0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning—0
Temporal Probabilistic Asymmetric Multi-task Learning—0
UNIVERSAL MODAL EMBEDDING OF DYNAMICS IN VIDEOS AND ITS APPLICATIONS—0
Adversarially learned anomaly detection for time series data—0
Detecting Change in Seasonal Pattern via Autoencoder and Temporal Regularization—0
Explaining Time Series by Counterfactuals—0
Interpretable Models for Understanding Immersive Simulations—0
WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time SeriesCode0
Recurrent Neural Network-based Model for Accelerated Trajectory Analysis in AIMD Simulations—0
Econometric modelling and forecasting of intraday electricity prices—0
Using Machine Learning to Predict Realized Variance—0
PyIT2FLS: A New Python Toolkit for Interval Type 2 Fuzzy Logic SystemsCode0
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution—0
Core Network Management Procedures for Self-Organized and Sustainable 5G Cellular Networks—0
Time Series Modeling for Dream Team in Fantasy Premier LeagueCode0
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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