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 3101–3125 of 6748 papers

TitleStatusHype
Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module—0
PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification—0
Testing Framework for Black-box AI Models—0
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice—0
Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak—0
Feature Selection for Multivariate Time Series via Network PruningCode0
Causal Inference for Time series Analysis: Problems, Methods and Evaluation—0
Anomaly Detection through Transfer Learning in Agriculture and Manufacturing IoT Systems—0
Last Query Transformer RNN for knowledge tracingCode1
Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting ModelsCode0
Attentive Gaussian processes for probabilistic time-series generation—0
Self-supervised learning for fast and scalable time series hyper-parameter tuning—0
Conditional Loss and Deep Euler Scheme for Time Series Generation—0
Concealer: SGX-based Secure, Volume Hiding, and Verifiable Processing of Spatial Time-Series Datasets—0
Forecasting Nonnegative Time Series via Sliding Mask Method (SMM) and Latent Clustered Forecast (LCF)—0
On Disentanglement in Gaussian Process Variational Autoencoders—0
NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series ForecastingCode1
Early Abandoning and Pruning for Elastic Distances including Dynamic Time WarpingCode1
Inductive Granger Causal Modeling for Multivariate Time Series—0
From sleep medicine to medicine during sleep: A clinical perspective—0
Bioluminescence modeling for deep sea experimentsCode0
Robust Optimization and Validation of Echo State Networks for learning chaotic dynamics—0
MALI: A memory efficient and reverse accurate integrator for Neural ODEsCode1
Meta-Learning for Koopman Spectral Analysis with Short Time-series—0
Reconstructing large networks with time-varying interactionsCode0
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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