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 1076–1100 of 6748 papers

TitleStatusHype
ARMA Cell: A Modular and Effective Approach for Neural Autoregressive ModelingCode1
Long-term hail risk assessment with deep neural networks—0
Learning Multiscale Non-stationary Causal Structures—0
Denoising Architecture for Unsupervised Anomaly Detection in Time-SeriesCode0
Modeling Volatility and Dependence of European Carbon and Energy Prices—0
Large-step neural network for learning the symplectic evolution from partitioned data—0
SoMoFormer: Multi-Person Pose Forecasting with TransformersCode1
Persistence Initialization: A novel adaptation of the Transformer architecture for Time Series Forecasting—0
Large-scale unsupervised spatio-temporal semantic analysis of vast regions from satellite images sequences—0
Understanding intra-day price formation process by agent-based financial market simulation: calibrating the extended chiarella model—0
High-frequency financial market simulation and flash crash scenarios analysis: an agent-based modelling approach—0
Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer ArchitecturesCode1
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
Global RTK Positioning in Graphical State SpaceCode1
A restricted eigenvalue condition for unit-root non-stationary data—0
Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image DenoisingCode0
A statistical test of market efficiency based on information theory—0
Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space ModelsCode0
Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings—0
DCSF: Deep Convolutional Set Functions for Classification of Asynchronous Time SeriesCode0
Spectrum of non-Hermitian deep-Hebbian neural networks—0
Time-to-Green predictions for fully-actuated signal control systems with supervised learning—0
Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory—0
Towards an Awareness of Time Series Anomaly Detection Models' Adversarial VulnerabilityCode0
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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