The growing population and effect of climate change have put a huge responsibility on the
agriculture sector to increase food-grain production and productivity. In most of the countries …

Abstract Machine learning mechanism is establishing itself as a promising area for
modelling and forecasting complex time series over conventional statistical models. In this …

Objectives The forecasting of horticulture commodity prices, such as bananas, has wide-
ranging impacts on farmers, traders and end-users. The considerable volatility in …

In this article, we employ a collection of stochastic differential equations with drift and
diffusion coefficients approximated by neural networks to predict the trend of chaotic time …

Advancements in various scientific fields have encouraged the development of novel tools,
techniques, components, methodologies, and innovations aimed at addressing the …

When forecasting financial time series, incorporating relevant sentiment analysis data into
the feature space is a common assumption to increase the capacities of the model. In …

The lack of information creates problems for Colombian small-scale farmers, as it impedes
them from selling at fair prices and knowing efficient production techniques. Around the …

The increasingly meagre copper ore resources constitute one of the decisive factors
influencing the price of this commodity. The demand for copper has been showing an …

In this paper, we propose seasonal long short-term memory (SLSTM), which is a method for
predicting the sales of agricultural products, to stabilize supply and demand. The SLSTM …

The paper presents a structure of the digital environment as an integral part of the “digital
twin” technology, and stipulates the research to be carried out towards an energy and …