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 751–775 of 6748 papers

TitleStatusHype
From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecastingCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
Diffusion-based Conditional ECG Generation with Structured State Space ModelsCode1
Dimensionality reduction to maximize prediction generalization capabilityCode1
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking ApplicationsCode1
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingCode1
DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local ExplanationsCode1
Diffusion models for missing value imputation in tabular dataCode1
From Time Series to Networks in R with the ts2net PackageCode1
Discovering Nonlinear Relations with Minimum Predictive Information RegularizationCode1
Price graphs: Utilizing the structural information of financial time series for stock predictionCode1
Principles and Algorithms for Forecasting Groups of Time Series: Locality and GlobalityCode1
Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic RegressionCode1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality ModelingCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Discrete Graph Structure Learning for Forecasting Multiple Time SeriesCode1
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
Full-Information Estimation of Heterogeneous Agent Models Using Macro and Micro DataCode1
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic PredictionCode1
ASTRIDE: Adaptive Symbolization for Time Series DatabasesCode1
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time seriesCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
Domain Adaptation for Time Series Under Feature and Label ShiftsCode1
An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series ClassificationCode1
AtsPy: Automated Time Series Forecasting in PythonCode1
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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