Picture a single forecasting mistake triggering a cascade of negative consequences, such as surplus inventory, strained supplier relationships and disappointed customers. In today's world, accurate ...

The authors analyze the interest rate risk in the banking book regulations, arguing that financial institutions must develop robust models for forecasting ...

Container throughput forecasting is critical for optimising port operations and maintaining the efficiency of global supply chains. Recent advances in machine learning have enabled researchers to ...

Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and economic uncertainty post-pandemic. Machine learning models can improve forecasting ...

Artificial intelligence-driven algorithms can be used to better forecast models for natural disasters, saving lives and protecting property by rapidly analyzing massive data sets and identifying ...

One of the unwritten axioms of data scientists specializing in machine learning methodologies is that they all try their hand at predicting the stock market. Some of the best attempts have turned a ...

The absence of reliable data on fundamental economic indicators (e.g. real GDP), combined with structural shifts in the economy, can severely constrain the ability to conduct accurate macroeconomic ...

In a new report sent to Rigzone this week, Standard Chartered revealed that it is launching a machine learning model for near-term Brent price forecasting. Dubbed SCORPIO (Standard Chartered Oil ...