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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4626–4650 of 6748 papers

TitleStatusHype
Graph Spectral Embedding for Parsimonious Transmission of Multivariate Time Series—0
Probabilistic sequential matrix factorizationCode0
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth MeasureCode0
Spatio-Temporal Alignments: Optimal transport through space and timeCode1
A Joint Model for IT Operation Series Prediction and Anomaly Detection—0
Continual Learning Using Bayesian Neural Networks—0
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies—0
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula ProcessesCode0
Patterns of Urban Foot Traffic Dynamics—0
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion—0
Unsupervised Representation for EHR Signals and Codes as Patient Status Vector—0
On the Duality between Network Flows and Network Lasso—0
DPSOM: Deep Probabilistic Clustering with Self-Organizing MapsCode0
Stationarity of the detrended price return in stock markets—0
The Neural Moving Average Model for Scalable Variational Inference of State Space ModelsCode0
Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels—0
MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive—0
End-to-end learning of energy-based representations for irregularly-sampled signals and imagesCode0
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
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
Machine Learning vs Statistical Methods for Time Series Forecasting: Size MattersCode0
Stochastic Weight Matrix-based Regularization Methods for Deep Neural NetworksCode0
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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