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 1851–1875 of 6748 papers

TitleStatusHype
Sparse Bayesian State-Space and Time-Varying Parameter Models—0
Dynamics and triggers of misinformation on vaccines—0
Forecasting euro area inflation using a huge panel of survey expectations—0
Extending the Range of Robust PCE Inflation Measures—0
LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning—0
Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression ModelingCode0
An NLP-Assisted Bayesian Time Series Analysis for Prevalence of Twitter Cyberbullying During the COVID-19 Pandemic—0
Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification—0
Time Series Prediction under Distribution Shift using Differentiable ForgettingCode0
Anomaly Detection for Fraud in Cryptocurrency Time Series—0
Fast strategies for multi-temporal speckle reduction of Sentinel-1 GRD images—0
Latent Space Unsupervised Semantic Segmentation—0
POP: Mining POtential Performance of new fashion products via webly cross-modal query expansionCode0
Time-Varying Poisson Autoregression—0
Multi-temporal speckle reduction with self-supervised deep neural networks—0
Exploring Financial Networks Using Quantile Regression and Granger Causality—0
MQRetNN: Multi-Horizon Time Series Forecasting with Retrieval Augmentation—0
Efficiency of the Moscow Stock Exchange before 2022—0
Estimating value at risk: LSTM vs. GARCH—0
Solving the optimal stopping problem with reinforcement learning: an application in financial option exerciseCode0
A Convolutional Neural Network Approach to Supernova Time-Series Classification—0
Probabilistic Reconciliation of Count Time Series—0
Using Neural Networks by Modelling Semi-Active Shock Absorber—0
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers—0
Task-aware Similarity Learning for Event-triggered 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