On the public citation graph

https://doi.org/10.2139/ssrn.4685440

0 signed · 30 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/1-84628-218-7_3openalex-extracted
Fundamentals of Statistical Signal Processing
inferredcites →https://doi.org/10.1007/s00521-021-06384-xopenalex-extracted
An adaptive backpropagation algorithm for long-term electricity load forecasting
inferredcites →https://doi.org/10.1007/s40866-020-00094-4openalex-extracted
Grey System Theory Based Net Load Forecasting for High Renewable Penetrated Power Systems
inferredcites →https://doi.org/10.1016/j.apenergy.2020.115410openalex-extracted
Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks
inferredcites →https://doi.org/10.1016/j.apenergy.2020.115440openalex-extracted
Machine learning based very short term load forecasting of machine tools
inferredcites →https://doi.org/10.1016/j.asoc.2020.106900openalex-extracted
Electrical load forecasting: A deep learning approach based on K-nearest neighbors
inferredcites →https://doi.org/10.1016/j.csda.2019.106907openalex-extracted
Logistic regression with missing covariates—Parameter estimation, model selection and prediction within a joint-modeling framework
inferredcites →https://doi.org/10.1016/j.egyr.2020.11.078openalex-extracted
A short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price
inferredcites →https://doi.org/10.1016/j.egyr.2020.11.148openalex-extracted
Study on power consumption load forecast based on K-means clustering and FCM–BP model
inferredcites →https://doi.org/10.1016/j.energy.2016.04.009openalex-extracted
An optimized grey model for annual power load forecasting
inferredcites →https://doi.org/10.1016/j.energy.2020.118499openalex-extracted
An improved seasonal GM(1,1) model based on the HP filter for forecasting wind power generation in China
inferredcites →https://doi.org/10.1016/j.epsr.2021.107173openalex-extracted
Convolutional and recurrent neural network based model for short-term load forecasting
inferredcites →https://doi.org/10.1016/j.epsr.2021.107604openalex-extracted
Saturated load forecasting based on clustering and logistic iterative regression
inferredcites →https://doi.org/10.1080/03610918.2017.1364384openalex-extracted
Performance of the almost unbiased ridge-type principal component estimator in logistic regression model
inferredcites →https://doi.org/10.1109/access.2017.2738029openalex-extracted
Short-Term Load-Forecasting Method Based on Wavelet Decomposition With Second-Order Gray Neural Network Model Combined With ADF Test
inferredcites →https://doi.org/10.1109/access.2020.3046105openalex-extracted
Saturated Demand Forecast of Regional Power Grid Based on Amended Self-Adaptive Logistic Model: A Case Study of East China
inferredcites →https://doi.org/10.1109/access.2021.3071654openalex-extracted
Deep Learning-Based Short-Term Load Forecasting Approach in Smart Grid With Clustering and Consumption Pattern Recognition
inferredcites →https://doi.org/10.1109/icicsp50920.2020.9232022openalex-extracted
Network Traffic Forecasting Based on Logistic Iterative Regression Model
inferredcites →https://doi.org/10.1109/jas.2021.1004075openalex-extracted
Learning Convex Optimization Models
inferredcites →https://doi.org/10.1109/jsyst.2016.2594208openalex-extracted
A Distributed Short-Term Load Forecasting Method Based on Local Weather Information
inferredcites →https://doi.org/10.1109/lsp.2018.2831178openalex-extracted
On the Maximum Likelihood Estimator Statistics for Unimodal Elliptical Distributions in the High Signal-to-Noise Ratio Regime
inferredcites →https://doi.org/10.1109/tii.2019.2930064openalex-extracted
Enhanced ELITE-Load: A Novel CMPSOATT Methodology Constructing Short-Term Load Forecasting Model for Industrial Applications
inferredcites →https://doi.org/10.1109/tsg.2018.2807985openalex-extracted
An Ensemble Forecasting Method for the Aggregated Load With Subprofiles
inferredcites →https://doi.org/10.1109/tsg.2018.2844307openalex-extracted
Short-Term Load Forecasting With Deep Residual Networks
inferredcites →https://doi.org/10.1126/science.1136800openalex-extracted
Clustering by Passing Messages Between Data Points
inferredcites →https://doi.org/10.3390/en13195210openalex-extracted
Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control
inferredcites →https://doi.org/10.3390/en14144107openalex-extracted
Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks
inferredcites →https://doi.org/10.3390/en14154525openalex-extracted
Electric Heating Load Forecasting Method Based on Improved Thermal Comfort Model and LSTM
inferredcites →https://doi.org/10.3390/info8010033openalex-extracted
Analysis and Modeling for China’s Electricity Demand Forecasting Based on a New Mathematical Hybrid Method
inferredcites →https://doi.org/10.48550/arxiv.1805.04602openalex-extracted
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.