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 2801–2825 of 6748 papers

TitleStatusHype
Fractional trends and cycles in macroeconomic time series—0
FRANS: Automatic Feature Extraction for Time Series Forecasting—0
Enhancing Energy System Models Using Better Load Forecasts—0
FreDo: Frequency Domain-based Long-Term Time Series Forecasting—0
Free congruence: an exploration of expanded similarity measures for time series data—0
Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems—0
Frequency Domain Compact 3D Convolutional Neural Networks—0
Frequency-Domain Stochastic Modeling of Stationary Bivariate or Complex-Valued Signals—0
Log-PDE Methods for Rough Signature Kernels—0
From BOP to BOSS and Beyond: Time Series Classification with Dictionary Based Classifiers—0
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning—0
A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting—0
From Generalization Analysis to Optimization Designs for State Space Models—0
From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?—0
Accelerating Neural ODEs Using Model Order Reduction—0
From learning gait signatures of many individuals to reconstructing gait dynamics of one single individual—0
Global Flood Prediction: a Multimodal Machine Learning Approach—0
From Rules to Regs: A Structural Topic Model of Collusion Research—0
From sleep medicine to medicine during sleep: A clinical perspective—0
From Static to Dynamic Node Embeddings—0
GP Kernels for Cross-Spectrum Analysis—0
From Time Series to Euclidean Spaces: On Spatial Transformations for Temporal Clustering—0
Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series—0
From time-series transcriptomics to gene regulatory networks: a review on inference methods—0
Enhance the performance of navigation: A two-stage machine learning approach—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