Forecasting Monthly Sales Retail Time Series: A Case Study

被引：11

作者：

Nunnari, Giuseppe ^{[1
]}

Nunnari, Valeria ^{[2
]}

机构：

[1] Univ Catania, Dipartimento Ingn Elettr Elettron & Informat, Viale A Doria 6, I-95125 Catania, Italy

[2] Zambon Co, Via Lillo del Duca 10, I-20091 Bresso, Italy

来源：

2017 IEEE 19TH CONFERENCE ON BUSINESS INFORMATICS (CBI), VOL 1 | 2017年 / 1卷

关键词：

CAR SALES;

D O I：

10.1109/CBI.2017.57

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a case study concerning the forecasting of monthly retail time series recorded by the US Census Bureau from 1992 to 2016. The modeling problem is tackled in two steps. First, original time series are de-trended by using a moving windows averaging approach. Subsequently, the residual time series are modeled by Non-linear Auto-Regressive (NAR) models, by using both Neuro-Fuzzy and Feed-Forward Neural Networks approaches. The goodness of the forecasting models, is objectively assessed by calculating the bias, the mae and the rmse errors. Finally, the model skill index is calculated considering the traditional persistent model as reference. Results show that there is a convenience in using the proposed approaches, compared to the reference one.

引用

页码：1 / 6

页数：6