Forecasting

"Mastering Forecasting in Machine Learning: A Journey through Crude Oil and Beyond" navigates the complex world of forecasting with engaging dialogues. From peeling layers in Chapter 1 to deploying predictive models in Chapter 11, the book demystifies the process. It explores data gathering, feature engineering, algorithmic magic, and model validation.

With the pressure of time-based competition increasing, and customers demanding faster service, availability of service parts becomes a critical component of manufacturing and servicing operations. Service Parts Management first focuses on intermittent demand forecasting and then on the management of service parts inventories. It guides researchers and practitioners in finding better management solutions to their problems and is both an excellent reference for key concepts and a leading resource for further research. Demand forecasting techniques are presented for parametric and nonparametric approaches, and multi echelon cases and inventory pooling are also considered. Inventory control is examined in the continuous and periodic review cases, while the following are all examined in the context of forecasting: • error measures, • distributional assumptions, and • decision trees. Service Parts Management provides the reader with an overview and a detailed treatment of the current state of the research available on the forecasting and inventory management of items with intermittent demand. It is a comprehensive review of service parts management and provides a starting point for researchers, postgraduate students, and anyone interested in forecasting or managing inventory.

This book focus on creating popularity and interest in modeling, derivation of equations for plant disease forecasting or construction and use of Web-based Expert Systems among plant pathologists. This book covers descriptions of many historic plant disease epidemics, various forecasting systems and methods of their construction, instruments required for study of plant disease epidemics, widely used commercial forecasting systems and present global scenario of forecasting.

In the human history plant disease epidemics have brought unsurmountable misery many a times. Still breaking out of epidemic in any time in any part of the world is a stark reality. The panic spraying of chemical pesticides is not a panacea. Only the IPM technology may give relief. This technology if backed by the disease forewarning system may yield the desired results. Hence, an in depth understanding of plant disease forecasting system and its successful implementation may bring the global food security.

This title provides a useful background for all students, practitioners, and researchers interested in the field of epidemiology, food security and agriculture sciences.

This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems.

Use R to work on real world time series analysis and forecasting examples. Applied data science with R.