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 3576–3600 of 6748 papers

TitleStatusHype
Stochastic density effects on adult fish survival and implications for population fluctuations—0
Stochastic Identification-based Active Sensing Acousto-Ultrasound SHM Using Stationary Time Series Models—0
Stochastic modelling of non-stationary financial assets—0
Stochastic Online Convex Optimization. Application to probabilistic time series forecasting—0
Stochastic Recurrent Neural Network for Multistep Time Series Forecasting—0
Stochastic Sequential Neural Networks with Structured Inference—0
Stock exchange shares ranking and binary-ternary compressive coding—0
Stock Performance Evaluation for Portfolio Design from Different Sectors of the Indian Stock Market—0
Stock Portfolio Optimization Using a Deep Learning LSTM Model—0
Stock Price Forecasting in Presence of Covid-19 Pandemic and Evaluating Performances of Machine Learning Models for Time-Series Forecasting—0
Stock price prediction using BERT and GAN—0
Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models—0
Stock Volatility Prediction using Time Series and Deep Learning Approach—0
STONet: A Neural-Operator-Driven Spatio-temporal Network—0
Storylines for structuring massive streams of news—0
Stream-Flow Forecasting of Small Rivers Based on LSTM—0
Streaming Linear System Identification with Reverse Experience Replay—0
Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning—0
STRIC: Stacked Residuals of Interpretable Components for Time Series Anomaly Detection—0
Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization—0
Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates—0
Structural Breaks in Time Series—0
Structural Change in (Economic) Time Series—0
Structural Damage Detection and Localization with Unknown Post-Damage Feature Distribution Using Sequential Change-Point Detection Method—0
Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning—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