Medium- to long-term nickel price forecasting using LSTM and GRU networks

Red chili is an agricultural commodity with high price volatility. Several previous studies stated that volatility was caused by weather effect on red chili production and shocks on public consumption. However, the other research stated that volatility was caused by the government’s import of red chili. This research aimed to analyze the price volatility of red chili in Semarang Regency on January 2019 to February 2020. The ARCH-GARCH method was applied in this study. This research showed that the price volatility of red chili occurred at the beginning, middle, and end of the year due to climate change, changes in public consumption patterns on religious holidays, and oversupply. However, the prevalence of Indonesia’s imports of red chili did not affect the price volatility. The government is suggested to implement a mapping policy and planting patterns to ensure the supply of red chili.

/pdf/arch-garch-analysis-an-approach-to-determine-the-price-14jmw14b.pdf

ARCH-GARCH Analysis: An Approach to Determine The Price Volatility of Red Chili

When it comes to energy sources used in electricity production, the future forecasting of electricity production from renewable energy sources is highly important for both the success of technological advancements in the renewable energy field and energy security. To forecast electricity production from renewable energy sources reliably, it is necessary to accurately model the components of the relevant series. The central argument of this paper is that the various components derived from electricity production data, particularly the residual component, retain valuable predictive information despite their intricate and nonlinear nature. While linear modelling may be highly accurate initially, repeating residuals within linear structures is a discrepancy in terms of data type and methodology. In this paper, different types of hybrid models that combine a decomposition method and both machine learning and statistical approaches are suggested for forecasting electricity production from different energy sources. 

Forecasting electricity production from various energy sources in Türkiye: A predictive analysis of time series, deep learning, and hybrid models

Cryptocurrency policy uncertainty and gold return forecasting: A dynamic Occam's window approach

A novel three-stage hybrid learning paradigm based on a multi-decomposition strategy, optimized relevance vector machine, and error correction for multi-step forecasting of precious metal prices

Gold price forecasting using multivariate stochastic model

Integrating Flexibility in Open Pit Mine Planning to Survive Commodity Price Decline

Mine Planning with Variable Price Input: A Case of Using Mine Planning Software

Integrating Stochastic Mine Planning Model with Ardl Commodity Price Forecasting

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Gold price forecasting using multivariate stochastic model

Integrating Flexibility in Open Pit Mine Planning to Survive Commodity Price Decline

Mine Planning with Variable Price Input: A Case of Using Mine Planning Software

Integrating Stochastic Mine Planning Model with Ardl Commodity Price Forecasting