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 2676–2700 of 6748 papers

TitleStatusHype
Signal processing on simplicial complexes—0
A Self-Supervised Framework for Function Learning and Extrapolation—0
Dynamic Asymmetric Causality Tests with an Application—0
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures—0
Graph Neural Network-Based Anomaly Detection in Multivariate Time SeriesCode1
Cross-Subject Domain Adaptation for Classifying Working Memory Load with Multi-Frame EEG Images—0
Geodesic Density Regression for Correcting 4DCT Pulmonary Respiratory Motion ArtifactsCode0
BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States from EEG Recordings—0
Semi-supervised Time Series Classification by Temporal Relation PredictionCode1
Multivariate Pair Trading by Volatility & Model Adaption Trade-off—0
Time Series Anomaly Detection with label-free Model Selection—0
Probability Paths and the Structure of Predictions over TimeCode0
Generative Adversarial Networks in finance: an overview—0
HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions—0
WAX-ML: A Python library for machine learning and feedback loops on streaming dataCode1
Recurrent Trend Predictive Neural Network for Multi-Sensor Fire DetectionCode1
A new measure between sets of probability distributions with applications to erratic financial behavior—0
Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties—0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
RNN with Particle Flow for Probabilistic Spatio-temporal ForecastingCode1
Machine Learning Framework for Sensing and Modeling Interference in IoT Frequency Bands—0
Multistep Electric Vehicle Charging Station Occupancy Prediction using Hybrid LSTM Neural Networks—0
GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series DataCode0
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data—0
Uncovering Closed-form Governing Equations of Nonlinear Dynamics from Videos—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