[HTML][HTML] Edible oil wholesale price forecasts via the neural network

X Xu, Y Zhang - Energy Nexus, 2023 - Elsevier
For a wide spectrum of agricultural market participants, building price forecasts of various
agricultural commodities has always been a vital project. In this work, we approach this …

Predicting wholesale edible oil prices through Gaussian process regressions tuned with Bayesian optimization and cross-validation

B **, X Xu - Asian Journal of Economics and Banking, 2024 - emerald.com
Purpose Develo** price forecasts for various agricultural commodities has long been a
significant undertaking for a variety of agricultural market players. The weekly wholesale …

[HTML][HTML] Comparison of ARIMA and GRU Models for High-Frequency Time Series Forecasting.

M Ridwan, K Sadik, FM Afendi - Scientific Journal of Informatics, 2023 - journal.unnes.ac.id
Purpose: The purpose of this research is to assess the efficacy of ARIMA and GRU models
in forecasting high-frequency stock price data, specifically minute-level stock data from …

[HTML][HTML] Impact Analysis of the External Shocks on the Prices of Malaysian Crude Palm Oil: Evidence from a Structural Vector Autoregressive Model

MS Sabri, N Khalid, AHM Azam, T Sarmidi - Mathematics, 2022 - mdpi.com
Palm oil prices, similar to other edible oils and commodity prices, are highly sensitive to
external shocks which have become particularly prominent in the wake of COVID-19 …

Comparison of fuzzy time forecasting multi-factor short cross associations with more partitions on the main factor and partitions on the second factor

ND Agustin, R Wulandari, B Surarso… - AIP Conference …, 2023 - pubs.aip.org
Economic decisions that have many determinants on the estimation of macroeconomic
variables require accurate estimates. These estimates are very influential on the results of …

Comparison of Kumar method and cross association method implemented in palm oil industry data

BK Aji, R Wulandari, B Irawanto, B Surarso… - AIP Conference …, 2024 - pubs.aip.org
Fuzzy time series has gained widespread popularity as a forecasting method in recent
years, with many researchers striving to enhance its performance by modifying clustering …