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 ...

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 ...

Blue Water Intelligence (BWI) has launched machine learning-enabled river flow forecasting and early warning services in ...

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 ...

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 ...

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 ...

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

Vassili Kitsios is a senior research scientist at CSIRO, a co-chair of the Machine Learning for Climate and Weather Working Group of the Australian Climate Community Earth System Simulator National ...