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 3301–3325 of 6748 papers

TitleStatusHype
Influential Node Detection in Implicit Social Networks using Multi-task Gaussian Copula Models—0
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers—0
Forecasting Using Reservoir Computing: The Role of Generalized Synchronization—0
Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem—0
Information-Aware Time Series Meta-Contrastive Learning—0
Information flow networks of Chinese stock market sectors—0
Information theoretical study of cross-talk mediated signal transduction in MAPK pathways—0
Information Theoretic Measures of Causal Influences during Transient Neural Events—0
Information Theory Inspired Pattern Analysis for Time-series Data—0
Complexity-based Financial Stress Evaluation—0
Forecasting under Long Memory and Nonstationarity—0
Forecasting trends with asset prices—0
Initial conditions in the neural field model—0
Initialising Kernel Adaptive Filters via Probabilistic Inference—0
Complexity and Persistence of Price Time Series of the European Electricity Spot Market—0
Initialization of multilayer forecasting artifical neural networks—0
Forecasting Time Series with VARMA Recursions on Graphs—0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems—0
Forecasting time series with encoder-decoder neural networks—0
Forecasting the Turkish Lira Exchange Rates through Univariate Techniques: Can the Simple Models Outperform the Sophisticated Ones?—0
Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting—0
Compensatory model for quantile estimation and application to VaR—0
A review of two decades of correlations, hierarchies, networks and clustering in financial markets—0
In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance—0
A Metamodel and Framework for Artificial General Intelligence From Theory to Practice—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