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 4976–5000 of 6748 papers

TitleStatusHype
Automatic Posterior Transformation for Likelihood-Free InferenceCode1
Semantic Analysis of Traffic Camera Data: Topic Signal Extraction and Anomalous Event Detection—0
Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature SpacesCode1
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks—0
Predicting Solar Flares Using a Long Short-Term Memory NetworkCode0
Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series—0
TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features—0
A self-organising eigenspace map for time series clustering—0
Online Anomaly Detection with Sparse Gaussian Processes—0
Aphids, Ants and Ladybirds: a mathematical model predicting their population dynamics—0
Similarity Grouping-Guided Neural Network Modeling for Maritime Time Series Prediction—0
A New Valuation Measure for the Stock MarketCode0
Long Short-Term Memory with Gate and State Level Fusion for Light Field-Based Face Recognition—0
Inferring Global Dynamics of a Black-Box System Using Machine Learning—0
Capturing Evolution Genes for Time Series Data—0
Time-Series Event Prediction with Evolutionary State GraphCode0
Large-Scale Spectrum Occupancy Learning via Tensor Decomposition and LSTM Networks—0
Differential Recurrent Neural Network and its Application for Human Activity Recognition—0
Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series ForecastingCode0
Unsupervised Learning through Temporal Smoothing and Entropy Maximization—0
Generalized Dilation Neural Networks—0
Learning Causality: Synthesis of Large-Scale Causal Networks from High-Dimensional Time Series Data—0
Temporal Attention Augmented Bilinear Network for Financial Time Series Data AnalysisCode0
Multivariate Time Series Classification using Dilated Convolutional Neural NetworkCode0
Convolution, attention and structure embedding—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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