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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 901–925 of 6748 papers

TitleStatusHype
Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian FilteringCode1
Time-Series Anomaly Detection Service at MicrosoftCode1
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulationsCode1
Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning—0
Anomalous volatility scaling in high frequency financial data—0
A Framework for Exploring Non-Linear Functional Connectivity and Causality in the Human Brain: Mutual Connectivity Analysis (MCA) of Resting-State Functional MRI with Convergent Cross-Mapping and Non-Metric Clustering—0
ANN Model to Predict Stock Prices at Stock Exchange Markets—0
An NLP-Assisted Bayesian Time Series Analysis for Prevalence of Twitter Cyberbullying During the COVID-19 Pandemic—0
A Framework for Evaluating the Impact of Food Security Scenarios—0
Actionable Interpretation of Machine Learning Models for Sequential Data: Dementia-related Agitation Use Case—0
An LLM-Powered Agent for Physiological Data Analysis: A Case Study on PPG-based Heart Rate Estimation—0
An L0-Norm Constrained Non-Negative Matrix Factorization Algorithm for the Simultaneous Disaggregation of Fixed and Shiftable Loads—0
A framework for anomaly detection using language modeling, and its applications to finance—0
An Investigation of the Structural Characteristics of the Indian IT Sector and the Capital Goods Sector: An Application of the R Programming in Time Series Decomposition and Forecasting—0
An investigation of higher order moments of empirical financial data and the implications to risk—0
A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns—0
Building Floorspace in China: A Dataset and Learning Pipeline—0
An Introductory Study on Time Series Modeling and Forecasting—0
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference—0
A Fourier Transform Approach for Automatic Detection of Oysters Spawning—0
A Formally Robust Time Series Distance Metric—0
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data—0
A Convolutional Neural Network Approach to Supernova Time-Series Classification—0
An interpretable LSTM neural network for autoregressive exogenous model—0
GeoStat Representations of Time Series for Fast Classification—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