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

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Showing 3801–3825 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
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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