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 6551–6600 of 6748 papers

TitleStatusHype
Exact Indexing of Time Series under Dynamic Time Warping—0
Exact Mean Computation in Dynamic Time Warping Spaces—0
Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detection—0
Exact Tests for Offline Changepoint Detection in Multichannel Binary and Count Data with Application to Networks—0
Examining Deep Learning Architectures for Crime Classification and Prediction—0
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting—0
Excess risk bound for deep learning under weak dependence—0
Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks—0
EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting—0
Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version—0
Expectation Propagation in Gaussian Process Dynamical Systems—0
Experimental demonstration of bandwidth enhancement in photonic time delay reservoir computing—0
Experimental design trade-offs for gene regulatory network inference: an in silico study of the yeast Saccharomyces cerevisiae cell cycle—0
Experimentally testable whole brain manifolds that recapitulate behavior—0
Expert Aggregation for Financial Forecasting—0
Explainable AI for clinical and remote health applications: a survey on tabular and time series data—0
Explainable AI for tailored electricity consumption feedback -- an experimental evaluation of visualizations—0
Explainable AI for Time Series via Virtual Inspection Layers—0
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey—0
Explainable boosted linear regression for time series forecasting—0
Explainable classification of astronomical uncertain time series—0
Explainable Failure Predictions with RNN Classifiers based on Time Series Data—0
Explainable Framework for Time-series Analysis via Topological Data Analysis—0
Explainable Multi-modal Time Series Prediction with LLM-in-the-Loop—0
Explainable nonlinear modelling of multiple time series with invertible neural networks—0
Q4EDA: A Novel Strategy for Textual Information Retrieval Based on User Interactions with Visual Representations of Time Series—0
Explainable Recommendation: Theory and Applications—0
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning—0
Explaining Outcomes of Multi-Party Dialogues using Causal Learning—0
Explaining Time Series by Counterfactuals—0
Explicit Domain Adaptation with Loosely Coupled Samples—0
Explicit-Duration Markov Switching Models—0
Explicitly Solvable Continuous-time Inference for Partially Observed Markov Processes—0
Exploiting multi-temporal information for improved speckle reduction of Sentinel-1 SAR images by deep learning—0
Exploiting Social Relations and Sentiment for Stock Prediction—0
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks—0
Exploiting statistical dependencies of time series with hierarchical correlation reconstruction—0
Exploiting the Power of Levenberg-Marquardt Optimizer with Anomaly Detection in Time Series—0
Exploiting Topic based Twitter Sentiment for Stock Prediction—0
Explorative Data Analysis of Time Series based AlgorithmFeatures of CMA-ES Variants—0
Exploring asymmetric multifractal cross-correlations of price-volatility and asymmetric volatility dynamics in cryptocurrency markets—0
Exploring Financial Networks Using Quantile Regression and Granger Causality—0
Exploring grid topology reconfiguration using a simple deep reinforcement learning approach—0
Exploring Long-Term Temporal Trends in the Use of Multiword Expressions—0
Exploring Physical-Based Constraints in Short-Term Load Forecasting: A Defense Mechanism Against Cyberattack—0
Exploring Predictive States via Cantor Embeddings and Wasserstein Distance—0
Exploring Self-Attention for Crop-type Classification Explainability—0
Exploring Spatial-Temporal Variations of Public Discourse on Social Media: A Case Study on the First Wave of the Coronavirus Pandemic in Italy—0
Exploring Strategies for Classification of External Stimuli Using Statistical Features of the Plant Electrical Response—0
Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications—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