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 5201–5225 of 6748 papers

TitleStatusHype
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial NetworksCode0
Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models—0
Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network MethodologyCode0
Forecasting Granular Audience Size for Online Advertising—0
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network—0
Causal Discovery with Attention-Based Convolutional Neural NetworksCode0
Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination—0
A unified framework of epidemic spreading prediction by empirical mode decomposition based ensemble learning techniques—0
A CNN adapted to time series for the classification of SupernovaeCode0
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class ClassificationCode0
Recurrent Neural Networks for Time Series Forecasting—0
Estimating information in time-varying signals—0
Comparison between DeepESNs and gated RNNs on multivariate time-series prediction—0
A General Deep Learning Framework for Network Reconstruction and Dynamics LearningCode0
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition—0
Forecasting Cardiology Admissions from Catheterization Laboratory—0
Using an Ancillary Neural Network to Capture Weekends and Holidays in an Adjoint Neural Network Architecture for Intelligent Building Management—0
Conditional heteroskedasticity in crypto-asset returns—0
Detecting British Columbia Coastal Rainfall Patterns by Clustering Gaussian Processes—0
Random selection of factors preserves the correlation structure in a linear factor model to a high degree—0
Image Embedding of PMU Data for Deep Learning towards Transient Disturbance Classification—0
NeuralWarp: Time-Series Similarity with Warping NetworksCode0
Feedforward Neural Network for Time Series Anomaly Detection—0
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time SeriesCode0
Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series ClassificationCode0
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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