데이터 길이 및 세분화에 따른수요 데이터별 최적의 분류모델 탐구
Exploration of Optimal Classification Models for Short time-series and Long time-series Retail Data with Granularity
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초록
소매유통 기업의 수요관리에서 수요예측의 중요성은 증가하고 있다. 시계열 데이터를 활용한 수요예측의 정확도를 높이기 위해서는 제품의 수요 특성을 이해하고 이에 따라 예측 모형을 구성하는 것이 필요하다. 다양한 종류의 제품을 판매하는 소매유통 기업은 개별 상품별로 시계열 수요예측 모형을 구성하기 어렵기 때문에 시계열 수요 특성이 동일하거나 유사한 상품군을 분류하고 상품군별로 최적의 예측 모형을 구성하는 방법이 요구된다. 본 연구에서는 수요의 유형 분류를 위하여 전통적인 SBC 방법과 같이 시계열 수요 데이터의 일부 통계량을 활용한 정적 수요 분류 방법의 한계를 보완하기 위하여 시계열 데이터를 직접 활용한 시계열 군집분석의 분류 성능을 평가하고자 한다. M5 competition에서 제공하는 총 1,246개 개별 상품의 2년치 판매 데이터를 대상으로 수치실험을 수행하였다. 수치실험 결과 다음과 같은 결과를 확인하였다. 첫째, Shape 기반 방법과 계층적 방법을 각각 유사도 측정 방법과 군집 구성 방법으로 사용한 시계열 군집분석 방법의 성능이 가장 우수하였다. 둘째, 전통적인 SBC 분류 방법은 직관적이고 계산이 용이한 장점이 있지만 시계열 수요 데이터의 특성을 반영하지 못하는 한계를 확인하였다. 셋째, 가용 데이터의 양과 데이터의 세분성 수준과 관계없이 시계열 군집분석 방법이 SBC 수요분류 방법 대비 우수한 성능을 보였다.
Abstract
The importance of demand forecasting is growing in retail industry. To improve the accuracy of demand forecasting using time series data, it is crucial to understand the demand characteristics and design forecasting models accordingly. Retail companies that sell a wide variety of products find it difficult to develop individual time series forecasting models for each product. Therefore, there is a need to classify products with similar or identical time series demand characteristics and develop optimal forecasting models for each product group. This study aims to evaluate the classification performance of time series clustering method, which directly utilizes time series data, to complement the limitations of traditional static demand classification methods using summary statistics such as the traditional SBC method. Numerical experiments were conducted using sales data for 1,246 individual products over two years, provided by the M5 competition. The results of the numerical experiments are as follows: First, the performance of time series clustering method, using Shape-based similarity measure and hierarchical clustering method, is the best. Second, while the traditional SBC classification method has the advantages of being intuitive and computationally simple, it was found to have limitations in reflecting the characteristics of time series demand. Third, regardless of the amount of available data or the level of data granularity, the time series clustering method showed superior performance compared to the SBC demand classification method.
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