A multi-series framework for demand forecasts in E-commerce
Sales forecasts are crucial for the E-commerce business. State-of-the-art techniques typically apply only univariate methods to make prediction for each series independently. However, due to the short nature of sales times series in E-commerce, univariate methods don't apply well. In this article, we propose a global model which outperforms state-of-the-art models on real dataset. It is achieved by using Tree Boosting Methods that exploit non-linearity and cross-series information. We also proposed a preprocessing framework to overcome the inherent difficulties in the E-commerce data. In particular, we use different schemes to limit the impact of the volatility of the data.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology
Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sa…
Time SeriesTime Series AnalysisA Multi-Phase Approach for Product Hierarchy Forecasting in Supply Chain Management: Application to MonarchFx Inc
Hierarchical time series demands exist in many industries and are often associated with the product, time frame, or geographic aggregations. Traditionally, these hierarchies have been forecasted using top-down, bottom-up…
ManagementTime SeriesTime Series AnalysisEventCast: Hybrid Demand Forecasting in E-Commerce with LLM-Based Event Knowledge
Demand forecasting is a cornerstone of e-commerce operations, directly impacting inventory planning and fulfillment scheduling. However, existing forecasting systems often fail during high-impact periods such as flash sa…
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
Time series prediction problems are becoming increasingly high-dimensional in modern applications, such as climatology and demand forecasting. For example, in the latter problem, the number of items for which demand need…
Demand ForecastingMissing ValuesTime SeriesTime Series Analysis+2Encoding Seasonal Climate Predictions for Demand Forecasting with Modular Neural Network
Current time-series forecasting problems use short-term weather attributes as exogenous inputs. However, in specific time-series forecasting solutions (e.g., demand prediction in the supply chain), seasonal climate predi…
Demand ForecastingTime SeriesTime Series Forecasting