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 701–725 of 6748 papers

TitleStatusHype
Unsupervised Flood Detection on SAR Time Series—0
Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods—0
Copula Conformal Prediction for Multi-step Time Series ForecastingCode1
Drift Identification for Lévy alpha-Stable Stochastic Systems—0
Denoising diffusion probabilistic models for probabilistic energy forecasting—0
A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting—0
A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks—0
A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection—0
In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time seriesCode0
Evaluation of Energy Resilience and Cost Benefit in Microgrid with Peer-to-Peer Energy Trading—0
Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection—0
Auxiliary Quantile Forecasting with Linear NetworksCode0
Deep Learning Architectures for FSCV, a Comparison—0
cs-net: structural approach to time-series forecasting for high-dimensional feature space data with limited observations—0
Complexity-based Financial Stress Evaluation—0
Observational and Interventional Causal Learning for Regret-Minimizing ControlCode0
Axial-LOB: High-Frequency Trading with Axial AttentionCode1
Contrastive Domain Adaptation for Time-Series via Temporal MixupCode1
The DOPE Distance is SIC: A Stable, Informative, and Computable Metric on Time Series And Ordered Merge Trees—0
Laplacian Convolutional Representation for Traffic Time Series Imputation—0
MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time SeriesCode1
RIPPLE: Concept-Based Interpretation for Raw Time Series Models in EducationCode0
Clustering individuals based on multivariate EMA time-series data—0
FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series ForecastingCode1
Diffusion Generative Models in Infinite DimensionsCode1
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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