Operational crop yield forecasting is mostly achieved with empirical statistical regression equations relating regional yield with predictor variables, termed “factors”. Regional yield (the “dependent variable”) refers to average yield over districts, provinces or, more rarely, whole countries; they are provided by national statistical services. The factors can be any combination of raw environ...

Introduction Reliable and timely forecasts provide important and useful input for proper, foresighted and informed planning, more so, in agriculture which is full of uncertainties. Agriculture now-adays has become highly input and cost intensive. Under the changed scenario today, forecasting of various aspects relating to agriculture are becoming essential. But in-spite of strong need for relia...

Forecasting future retail sales is one of the most important activities that form the basis for all strategic and planning decisions in effective operations of retail businesses as well as retail supply chains. This chapter illustrates how to best model and forecast retail sales time series that contain both trend and seasonal variations. The effectiveness of data preprocessing such as detrendi...

We consider forecasting the latent rate profiles of a time series of inhomogeneous Poisson processes. The work is motivated by operations management of queueing systems, in particular, telephone call centers, where accurate forecasting of call arrival rates is a crucial primitive for efficient staffing of such centers. Our forecasting approach utilizes dimension reduction through a factor analy...

Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to explore the dynamics of neural networks in forecasting crop (tomato) yield using environmental variables; here we aim at giving accurate yield amount. We use the Adaptive Neuro-Fuzzy Inference System (ANFIS). The input to ANFIS is several parameters de...