Forecasting with Big Data: A Review

Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. In this paper we present a comprehensive review on the use of Big Data for forecasting by identifying and reviewing the problems, potential, challenges and most importantly the related applications. Skills, hardware and software, algorithm architecture, statistical significance, the signal to noise ratio and the nature of Big Data itself are identified as the major challenges which are hindering the process of obtaining meaningful forecasts from Big Data. The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks are the most common tools adopted for forecasting with Big Data

Staffing decisions for heterogeneous workers with turnover

In this paper we consider a firm that employs heterogeneous workers to meet demand for its product or service. Workers differ in their skills, speed, and/or quality, and they randomly leave, or turn over. Each period the firm must decide how many workers of each type to hire or fire in order to meet randomly changing demand forecasts at minimal expense. When the number of workers of each type can by continuously varied, the operational cost is jointly convex in the number of workers of each type, hiring and firing costs are linear, and a random fraction of workers of each type leave in each period, the optimal policy has a simple hire- up-to/fire-down-to structure. However, under the more realistic assumption that the number of workers of each type is discrete, the optimal policy is much more difficult to characterize, and depends on the particular notion of discrete convexity used for the cost function. We explore several different notions of discrete convexity and their impact on structural results for the optimal policy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45844/1/186_2005_Article_33.pd