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Found 9 papers, 2 papers with code
MapTrack: Tracking in the Map
The prediction map determines whether an object is in a crowd, and we prioritize state estimations over observations when severe deformation of observations occurs, accomplished through the covariance adaptive Kalman filter.
Electricity Demand Forecasting through Natural Language Processing with Long Short-Term Memory Networks
This paper proposes a Long and Short-Term Memory (LSTM) network incorporating textual news features that successfully predicts the deterministic and probabilistic tasks of the UK national electricity demand.
Compact Twice Fusion Network for Edge Detection
In this work, we propose a Compact Twice Fusion Network (CTFN) to fully integrate multi-scale features while maintaining the compactness of the model.
News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand
This study explores the link between electricity demand and more nuanced information about social events.
A hybrid ensemble method with negative correlation learning for regression
It is also possible to improve the NCL ensemble with a regularization term in the objective function.
Exploring the social influence of Kaggle virtual community on the M5 competition
This paper aims to study the social influence of virtual community on user behaviors in the M5 competition.
Crude oil price forecasting incorporating news text
Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA (latent Dirichlet allocation) designed for accommodating long text to discover knowledge from them.
Research and application of time series algorithms in centralized purchasing data
Based on the online transaction data of COSCO group's centralized procurement platform, this paper studies the clustering method of time series type data.
Loop Restricted Existential Rules and First-order Rewritability for Query Answering
In particular, we prove that this language satisfies the so-called bounded derivation-depth prop- erty (BDDP), which implies that the CQA is first-order rewritable, and its data complexity is in AC0 .
