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 1251–1300 of 6748 papers

TitleStatusHype
Task-Oriented Prediction and Communication Co-Design for Haptic Communications—0
TherapyView: Visualizing Therapy Sessions with Temporal Topic Modeling and AI-Generated Arts—0
Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting—0
Toward Asymptotic Optimality: Sequential Unsupervised Regression of Density Ratio for Early ClassificationCode0
CNTS: Cooperative Network for Time SeriesCode0
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification—0
Dynamic Graph Neural Network with Adaptive Edge Attributes for Air Quality Predictions—0
Estimating Treatment Effects in Continuous Time with Hidden Confounders—0
Ergodic characterization of non-ergodic anomalous diffusion processes—0
Redes Generativas Adversarias (GAN) Fundamentos Teóricos y Aplicaciones—0
Quantile LSTM: A Robust LSTM for Anomaly Detection In Time Series Data—0
Graphical estimation of multivariate count time series—0
A Transformer-based Deep Learning Algorithm to Auto-record Undocumented Clinical One-Lung Ventilation Events—0
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models—0
Functional Connectivity Dynamics show Resting-State Instability and Rightward Parietal Dysfunction in ADHD—0
Excess risk bound for deep learning under weak dependence—0
Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand—0
Online Detection of Changes in Moment-Based Projections: When to Retrain Deep Learners or Update Portfolios?—0
Fourier-RNNs for Modelling Noisy Physics Data—0
Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes—0
Label-efficient Time Series Representation Learning: A Review—0
Continuous-time convolutions model of event sequencesCode0
Forecasting the Turkish Lira Exchange Rates through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?—0
Interpretable Deep Learning for Forecasting Online Advertising Costs: Insights from the Competitive Bidding Landscape—0
SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies—0
ShapeWordNet: An Interpretable Shapelet Neural Network for Physiological Signal Classification—0
Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates—0
Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking—0
Estimating Driver Personality Traits from On-Road Driving Data—0
Dynamic and Stochastic Rational Behavior—0
Short-Term Memory Convolutions—0
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks—0
High-Dimensional Granger Causality for Climatic Attribution—0
CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series DataCode0
Unsupervised Deep Learning for IoT Time Series—0
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series—0
ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information—0
Tree-Based Learning on Amperometric Time Series Data Demonstrates High Accuracy for Classification—0
Noise-cleaning the precision matrix of fMRI time series—0
Importance attribution in neural networks by means of persistence landscapes of time series—0
Identifiability of latent-variable and structural-equation models: from linear to nonlinear—0
Market-Based Probability of Stock Returns—0
Penalized Quasi-likelihood Estimation and Model Selection in Time Series Models with Parameters on the Boundary—0
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust—0
Cross-Frequency Time Series Meta-Forecasting—0
Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting—0
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization—0
FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting—0
Inference in Non-stationary High-Dimensional VARs—0
A comparative study of statistical and machine learning models on near-real-time daily emissions prediction—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