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

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Showing 2801–2825 of 6748 papers

TitleStatusHype
Apply Artificial Neural Network to Solving Manpower Scheduling ProblemCode0
Finite volume method network for acceleration of unsteady computational fluid dynamics: non-reacting and reacting flows—0
Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media—0
On the Time Series Length for an Accurate Fractal Analysis in Network Systems—0
Neural graphical modelling in continuous-time: consistency guarantees and algorithmsCode1
Estimating Reproducible Functional Networks Associated with Task Dynamics using Unsupervised LSTMs—0
Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases—0
Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange ResidualCode0
Granger Causality: A Review and Recent Advances—0
Reconstructing shared dynamics with a deep neural networkCode0
Order flow in the financial markets from the perspective of the Fractional Lévy stable motion—0
Stock Price Forecasting in Presence of Covid-19 Pandemic and Evaluating Performances of Machine Learning Models for Time-Series Forecasting—0
Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and AutoencodersCode0
An Estimation of Online Video User Engagement from Features of Continuous Emotions—0
A Two-Stage Coordinative Zonal Volt/VAR Control Scheme for Distribution Systems with High Inverter-based Resources—0
Signal automata and hidden Markov models—0
Using Twitter Attribute Information to Predict Stock PricesCode1
TimeGym: Debugging for Time Series Modeling in Python—0
COUnty aggRegation mixup AuGmEntation (COURAGE) COVID-19 Prediction—0
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series—0
Context-aware demand prediction in bike sharing systems: incorporating spatial, meteorological and calendrical context—0
Process Model Forecasting Using Time Series Analysis of Event Sequence DataCode0
Comparison Analysis of Facebook's Prophet, Amazon's DeepAR+ and CNN-QR Algorithms for Successful Real-World Sales Forecasting—0
Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks—0
All-Clear Flare Prediction Using Interval-based Time Series Classifiers—0
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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