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 5151–5175 of 6748 papers

TitleStatusHype
On Clustering Time Series Using Euclidean Distance and Pearson Correlation—0
On Computational Complexity Reduction Methods for Kalman Filter Extensions—0
On Consistency and Asymptotic Normality of Least Absolute Deviation Estimators for 2-dimensional Sinusoidal Model—0
On Designing Data Models for Energy Feature Stores—0
On Disentanglement in Gaussian Process Variational Autoencoders—0
On Early-stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners—0
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks—0
One-dimensional Deep Image Prior for Time Series Inverse Problems—0
One model is not enough: heterogeneity in cryptocurrencies' multifractal profiles—0
ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting—0
On Error Correction Neural Networks for Economic Forecasting—0
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing—0
One-Step or Two-Step Optimization and the Overfitting Phenomenon: A Case Study on Time Series Classification—0
On Estimation and Inference of Large Approximate Dynamic Factor Models via the Principal Component Analysis—0
On Improving Deep Reinforcement Learning for POMDPs—0
On LASSO for High Dimensional Predictive Regression—0
Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach—0
Online Anomaly Detection with Sparse Gaussian Processes—0
Online change-point detection with kernels—0
Online Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems—0
Online Debiasing for Adaptively Collected High-dimensional Data with Applications to Time Series Analysis—0
Online deforestation detection—0
Online Detection of Changes in Moment-Based Projections: When to Retrain Deep Learners or Update Portfolios?—0
Online detection of failures generated by storage simulator—0
Online estimation methods for irregular autoregressive models—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