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 2051–2100 of 6748 papers

TitleStatusHype
Classifying Human Activities using Machine Learning and Deep Learning Techniques—0
Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics—0
Dependent Latent Class ModelsCode0
Financial Time Series Data Augmentation with Generative Adversarial Networks and Extended Intertemporal Return Plots—0
GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints—0
Achieving Risk Control in Online Learning SettingsCode0
Markov Chain Monte Carlo for Continuous-Time Switching Dynamical Systems—0
Forecasting Solar Power Generation on the basis of Predictive and Corrective Maintenance Activities—0
Automated Mobility Context Detection with Inertial Signals—0
A Data Cube of Big Satellite Image Time-Series for Agriculture MonitoringCode0
Multi-scale Attention Flow for Probabilistic Time Series Forecasting—0
TNN7: A Custom Macro Suite for Implementing Highly Optimized Designs of Neuromorphic TNNsCode0
Joint cardiac T_1 mapping and cardiac function estimation using a deep manifold framework—0
Market-Based Asset Price Probability—0
Statistical Modeling and Forecasting of Automatic Generation Control Signals—0
Nonparametric Value-at-Risk via Sieve EstimationCode0
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks—0
Modelling stellar activity with Gaussian process regression networksCode0
Method of indirect estimation of default probability dynamics for industry-target segments according to the data of Bank of Russia—0
Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training—0
An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics—0
Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs—0
Characterization of electric consumers through an automated clustering pipeline—0
Inferring Density-Dependent Population Dynamics Mechanisms through Rate Disambiguation for Logistic Birth-Death ProcessesCode0
Deep Federated Anomaly Detection for Multivariate Time Series Data—0
On Designing Data Models for Energy Feature Stores—0
Policy Choice in Time Series by Empirical Welfare Maximization—0
Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction—0
Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data—0
Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance—0
Anomaly Detection in Intra-Vehicle Networks—0
Stock Price Prediction Based on Natural Language ProcessingCode0
Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery—0
Summary Markov Models for Event Sequences—0
LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding—0
KnitCity: a machine learning-based, game-theoretical framework for prediction assessment and seismic risk policy design—0
GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data—0
COVID-19 epidemiology as emergent behavior on a dynamic transmission forestCode0
Multi-Spatio-temporal Fusion Graph Recurrent Network for Traffic forecasting—0
A walk through of time series analysis on quantum computers—0
DeepGraviLens: a Multi-Modal Architecture for Classifying Gravitational Lensing DataCode0
Differentially Private Multivariate Time Series Forecasting of Aggregated Human Mobility With Deep Learning: Input or Gradient Perturbation?Code0
Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake Detection—0
Incorporating Stock Market Signals for Twitter Stance DetectionCode0
Neural Machine Translation for Fact-checking Temporal Claims—0
Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation—0
Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell GenomicsCode0
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series ForecastCode0
The Cross-Sectional Intrinsic Entropy. A Comprehensive Stock Market Volatility Estimator—0
Sparse-Group Log-Sum Penalized Graphical Model Learning For Time Series—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