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 726–750 of 6748 papers

TitleStatusHype
Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood RiskCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
Hierarchical forecasting with a top-down alignment of independent level forecastsCode1
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro DataCode1
Finding active galactic nuclei through FinkCode1
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease ProgressionCode1
Deep Semi-Supervised Learning for Time Series ClassificationCode1
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data ProcessingCode1
Finding Scientific Topics in Continuously Growing Text CorporaCode1
Deeptime: a Python library for machine learning dynamical models from time series dataCode1
Diffusion-based Conditional ECG Generation with Structured State Space ModelsCode1
Nonlinear proper orthogonal decomposition for convection-dominated flowsCode1
Financial Time Series Data Processing for Machine LearningCode1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic PredictionCode1
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
Delhi air quality prediction using LSTM deep learning models with a focus on COVID-19 lockdownCode1
A Multi-view Multi-task Learning Framework for Multi-variate Time Series ForecastingCode1
NTS-NOTEARS: Learning Nonparametric DBNs With Prior KnowledgeCode1
Financial time series forecasting with multi-modality graph neural networkCode1
On Contrastive Representations of Stochastic ProcessesCode1
DEPTS: Deep Expansion Learning for Periodic Time Series ForecastingCode1
FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable StocksCode1
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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