Time series forecasting, bolstered by models such as ARIMA, SARIMA and LSTM, ensures that decisions are made based on robust data analytics rather than mere chance.

As retail organizations navigate shifting consumer expectations and supply chain complexities, the role of data and ...

A hybrid ensemble learning approach is proposed for financial time series forecasting combining AdaBoost algorithm and long short-term memory (LSTM) network. First, LSTM predictor is trained using the ...

During the COVID-19 crisis period, when GDP growth became unusually volatile, the advantages of deep learning became even ...

The 2023 paper “Time Series-Based Quantitative Risk Models: Enhancing Accuracy in Forecasting and Risk Assessment” by Olanrewaju Olukoya Odumuwagun, published in the International Journal of ...