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 1276–1300 of 6748 papers

TitleStatusHype
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