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 3101–3125 of 6748 papers

TitleStatusHype
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
Fractional trends and cycles in macroeconomic time series—0
Compressive Nonparametric Graphical Model Selection For Time Series—0
Fractional SDE-Net: Generation of Time Series Data with Long-term Memory—0
How Much Can A Retailer Sell? Sales Forecasting on Tmall—0
How News Evolves? Modeling News Text and Coverage using Graphs and Hawkes Process—0
How Noisy Social Media Text, How Diffrnt Social Media Sources?—0
Extending the Range of Robust PCE Inflation Measures—0
Fractional integration and cointegration—0
Comprehensive Time-Series Regression Models Using GRETL -- U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013—0
How to monitor and mitigate stair-casing in l1 trend filtering—0
A metric to compare the anatomy variation between image time series—0
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems—0
Fractional Growth Portfolio Investment—0
HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction—0
Fractal Time Series Analysis of Social Network Activities—0
Human activity recognition based on time series analysis using U-Net—0
Comprehensive Review of Neural Differential Equations for Time Series Analysis—0
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks—0
Fractal structures in Adversarial Prediction—0
Human Activity Recognition using Smartphone—0
Fractal approach towards power-law coherency to measure cross-correlations between time series—0
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions—0
Fractal analyses of networks of integrate-and-fire stochastic spiking neurons—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