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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

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Showing 3801–3850 of 6748 papers

TitleStatusHype
The statistical physics of discovering exogenous and endogenous factors in a chain of events—0
The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification—0
The tourism forecasting competition—0
The Use of Gaussian Processes in System Identification—0
The use of scaling properties to detect relevant changes in financial time series: a new visual warning tool—0
The VVAD-LRS3 Dataset for Visual Voice Activity Detection—0
The Western Africa Ebola virus disease epidemic exhibits both global exponential and local polynomial growth rates—0
The Wiki Music dataset: A tool for computational analysis of popular music—0
Three Remarks On Asset Pricing—0
Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model—0
Thresholded ConvNet Ensembles: Neural Networks for Technical Forecasting—0
Time Adaptive Gaussian Model—0
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series—0
TimeCSL: Unsupervised Contrastive Learning of General Shapelets for Explorable Time Series Analysis—0
Time Delay Estimation of Traffic Congestion Based on Statistical Causality—0
Time-dependent scaling patterns in high frequency financial data—0
Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps—0
Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting—0
Time Gated Convolutional Neural Networks for Crop Classification—0
TimeGym: Debugging for Time Series Modeling in Python—0
Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series—0
Time-Incremental Learning from Data Using Temporal Logics—0
Time is limited on the road to asymptopia—0
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data—0
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering—0
TimePool: Visually Answer "Which and When" Questions On Univariate Time Series—0
TimeREISE: Time-series Randomized Evolving Input Sample Explanation—0
Time Scale Network: A Shallow Neural Network For Time Series Data—0
Time scales in stock markets—0
Time-Series Adaptive Estimation of Vaccination Uptake Using Web Search Queries—0
Time Series Analysis and Forecasting of COVID-19 Cases Using LSTM and ARIMA Models—0
Time Series Analysis and Forecasting of the US Housing Starts using Econometric and Machine Learning Model—0
Time Series Analysis by State Space Learning—0
Time Series Analysis in American Stock Market Recovering in Post COVID-19 Pandemic Period—0
Time Series Analysis in Compressor-Based Machines: A Survey—0
Time Series Analysis of Big Data for Electricity Price and Demand to Find Cyber-Attacks part 2: Decomposition Analysis—0
Time Series Analysis of Electricity Price and Demand to Find Cyber-attacks using Stationary Analysis—0
Time Series Analysis of Key Societal Events as Reflected in Complex Social Media Data Streams—0
Time Series Analysis of Rankings: A GARCH-Type Approach—0
Time Series Analysis of Urban Liveability—0
Time-Series Analysis on Edge-AI Hardware for Healthcare Monitoring—0
Time-Series Analysis via Low-Rank Matrix Factorization Applied to Infant-Sleep Data—0
Time Series Analysis via Network Science: Concepts and Algorithms—0
Time series analysis with dynamic law exploration—0
Time series and machine learning to forecast the water quality from satellite data—0
Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data—0
Time Series Anomaly Detection for Smart Grids: A Survey—0
Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach—0
Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning—0
Time Series Anomaly Detection with label-free Model Selection—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