Demand forecasting: an alternative approach based on technical indicator Pbands

Research background: Demand forecasting helps companies to anticipate purchases and plan the delivery or production. In order face this complex problem, many statistical methods, artificial intelligence-based hybrid methods are currently being developed. However, all these have similar problematic issues, including complexity, long computing time, need for high performance of IT infrastructure.
 Purpose article: This study aims verify evaluate possibility using Google Trends data poetry book demand compare results application neural networks, a model versus alternative technical analysis achieve immediate accessible forecasting. Specifically, it based on an approach Pbands indicator books in European Quartet countries.
 Methods: The performs search case keyword countries by several commonly used ETS ARIMA method, ARFIMA BATS method combination Cox-Box transformation ARMA, Theta model, indicator. uses MAPE RMSE approaches measure accuracy.
 Findings & value added: Although most available prediction models either slow complex, entrepreneurial practice requires fast, simple, accurate ones. show that is easily applicable can predict short-term changes. Due its simplicity, suitable convenient monitor describing demand. indicators represent new could be further research direction. future theoretical should devoted mainly simplifying speeding up. Creating automated primary parameters interpretable challenge research.