Forecasting, Volume 7, Issue 4

Unconfined Compressive Strength (UCS) of stabilized soils is commonly used for evaluating the effectiveness of soil improvement techniques. Achieving target UCS values through conventional trial-and-error approaches requires extensive laboratory expe...

This research tackles the challenge of forecasting nonlinear time series data with stochastic structural variations by proposing the Markov switching autoregressive model with time-varying parameters (MSAR-TVP). Although effective in modeling dynamic...

Pharmaceutical manufacturing and logistics rely on accurate prediction and decision making to safeguard product quality, delivery reliability, and patient outcomes. Despite rapid advances in artificial intelligence (AI) and machine learning (ML), few...

This study explores the role of decentralized physical infrastructure networks (DePINs) in enhancing solar energy forecasting, focusing on how network density influences prediction accuracy and economic viability. Using machine learning models applie...

This study introduces a novel k-nearest neighbors (kNN) method of forecasting precipitation at weather-observing stations. The method identifies numerous monthly temporal patterns to produce precipitation forecasts for a specific month. Compared to c...

Time series models are considered among the most intricate models in machine learning. Due to sharp temporal variations, time series models normally fall short in predicting the peaks or local minima accurately. To overcome this challenge, we propose...

This paper presents a novel system dynamics-based framework for forecasting market share evolution in the telecommunications sector. The framework conceptualizes market share as flows of subscribers—driven by churn, attraction, and market growt...

The automobile industry is one of the world’s largest manufacturing sectors and a key contributor to economic growth. Demand forecasting plays a critical role in supply chain management within the automotive sector. Reliable forecasts are essen...

Accurately predicting carbon trading price is challenging due to pronounced nonlinearity, non-stationarity, and sensitivity to diverse factors, including macroeconomic conditions, market sentiment, and climate policy. This study proposes a novel hybr...

Classical time series methods are widely employed to analyze linear time series with a limited number of observations; however, their effectiveness relies on several strict assumptions. In contrast, artificial neural networks are particularly suitabl...