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
InfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers—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
Data-driven Stabilization of Discrete-time Control-affine Nonlinear Systems: A Koopman Operator Approach—0
Causal Analysis of Generic Time Series Data Applied for Market Prediction—0
Data-Driven Time Series Reconstruction for Modern Power Systems Research—0
Initial conditions in the neural field model—0
Initialising Kernel Adaptive Filters via Probabilistic Inference—0
Initialization matters: Orthogonal Predictive State Recurrent Neural Networks—0
Initialization of multilayer forecasting artifical neural networks—0
Irregularly-Sampled Time Series Modeling with Spline Networks—0
Injecting Explainability and Lightweight Design into Weakly Supervised Video Anomaly Detection Systems—0
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting—0
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection—0
A Graph Neural Networks based Framework for Topology-Aware Proactive SLA Management in a Latency Critical NFV Application Use-case—0
Data manipulation detection via permutation information theory quantifiers—0
Causal analysis of Covid-19 Spread in Germany—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
Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method—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