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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
Covariance-engaged Classification of Sets via Linear Programming—0
House Price Prediction Using LSTM—0
Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary—0
Empowering Time Series Analysis with Large Language Models: A Survey—0
How macroscopic laws describe complex dynamics: asymptomatic population and CoviD-19 spreading—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
Empowering Time Series Analysis with Synthetic Data: A Survey and Outlook in the Era of Foundation Models—0
Causal Discovery from Subsampled Time Series Data by Constraint Optimization—0
How to monitor and mitigate stair-casing in l1 trend filtering—0
雜訊環境下應用線性估測編碼於特徵時序列之強健性語音辨識 (Employing Linear Prediction Coding in Feature Time Sequences for Robust Speech Recognition in Noisy Environments) [In Chinese]—0
Empirics on the expressiveness of Randomized Signature—0
How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events—0
HQNN-FSP: A Hybrid Classical-Quantum Neural Network for Regression-Based Financial Stock Market Prediction—0
Huber Additive Models for Non-stationary Time Series Analysis—0
Human activity recognition based on time series analysis using U-Net—0
Causal Discovery from Sparse Time-Series Data Using Echo State Network—0
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks—0
COVID-19 infection and recovery in various countries: Modeling the dynamics and evaluating the non-pharmaceutical mitigation scenarios—0
Human Activity Recognition using Smartphone—0
Phase-randomised Fourier transform model for the generation of synthetic wind speeds—0
Empirical Studies on Symbolic Aggregation Approximation Under Statistical Perspectives for Knowledge Discovery in Time Series—0
Empirical Risk Minimization for Time Series: Nonparametric Performance Bounds for Prediction—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