Weighted Averages and Polynomial Interpolation for PM2.5 Time Series Forecasting

Weighted Averages and Polynomial Interpolation for PM2.5 Time Series Forecasting

This article describes a novel method for the multi-step forecasting of PM2.5 time series based on weighted averages and polynomial interpolation. Multi-step prediction models enable decision makers to build an understanding of longer future terms than the one-step-ahead prediction models, allowing...

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Bibliographic Details
Main Authors: Anibal Flores, Hugo Tito-Chura, Victor Yana-Mamani, Charles Rosado-Chavez, Alejandro Ecos-Espino
Format: Article
Language:English
Published: MDPI AG 2024-09-01
Series:Computers
Subjects:
PM2.5
multi-step forecasting
weighted averages
polynomial interpolation
deep learning
Online Access:https://www.mdpi.com/2073-431X/13/9/238
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