M5 accuracy competition: Results,findings, and conclusions

In this study, we present the results of the M5 “Accuracy” competition, which was the first of two parallel challenges in the latest M competition with the aim of advancing the theory and practice of forecasting. The main objective in the M5 “Accuracy” competition was to accurately predict 42,840 time series representing the hierarchical unit sales for the largest retail company in the world by revenue, Walmart. The competition required the submission of 30,490 point forecasts for the lowest cross-sectional aggregation level of the data, which could then be summed up accordingly to estimate forecasts for the remaining upward levels. We provide details of the implementation of the M5 “Accuracy” challenge, as well as the results and best performing methods, and summarize the major findings and conclusions. Finally, we discuss the implications of these findings and suggest directions for future research.     

A simple combination of univariate models

This paper describes the approach that we implemented for producing the point forecasts and prediction intervals for our M4-competition submission. The proposed simple combination of univariate models (SCUM) is a median combination of the point forecasts and prediction intervals of four models, namely exponential smoothing, complex exponential smoothing, automatic autoregressive integrated moving average and dynamic optimised theta. Our submission performed very well in the M4-competition, being ranked 6^th for the point forecasts (with a small difference compared to the 2^nd submission) and prediction intervals and 2^nd and 3^rd for the point forecasts of the weekly and quarterly data respectively.     

从“一稿多投”的法律定性思考著作权立法问题

对于"一稿多投"泛化现象已十分普遍。对于其合法性问题,在学界也存在很大争议。针对这一矛盾,下面将对"一稿多投"行为进行分类定性,来分析和界定其法律性质,为著作权立法问题提供一个有效解决途径,更好实现投稿人与采稿人之间合法利益的衡平。     

一种开源环境的在线投稿系统

在线投稿系统因其方便、快捷、高效等特点已成为稿件管理的主要趋势和重要途径,但为使用者开发一个度身定制的投稿系统,往往价格不菲,而且后续维护和技术支持又常常受制于人。因此,本文针对中小型编辑部管理者设计并开发了一种以ASP和MYSQL为开发环境,采用B/S模式的在线投稿系统,该系统体积小,适应性好,功能完善,维护门槛低,一般高校网管人员即可胜任,还可根据用户需求进行功能扩展,具有良好的应用前景。     

建筑物沉降的组合预测模型应用研究

提出采用组合模型预测建筑物沉降,并以常用的三种建筑物沉降预测模型组成组合模型进行了计算分析,结果表明组合预测模型具有较高的预测精度,值得推广应用.     

我国铁路客运量的组合预测

首先对我国月度铁路客运量的变动特征进行了分析,并结合多元线性回归和时间序列预测两种模型,运用组合预测方法对2009年1月至12月的铁路客运量进行了预测,结果表明预测误差小,组合预测精度相对单个预测方法均有所提高,说明组合预测是月度铁路客运量预测的有效方法。     

具有任意投资水平的供应链协同预测博弈研究

运用纳什均衡和贝叶斯更新模型,得到了供应链联合预测均衡的存在条件。模型中,供应商和零售商均需决定在预测技术上的投资水平,双方的需求预测将会被汇总成一个统一的预测。结果表明,双方预测能力越接近中等水平,越容易实现联合预测。预测能力偏离中等水平越远,越容易出现搭便车行为,即至少有一方不进行预测。     

Macroeconomic forecasting using structural factor analysis

The use of a small number of underlying factors to summarize the information from a much larger set of information variables is one of the new frontiers in forecasting. In prior work, the estimated factors have not usually had a structural interpretation and the factors have not been chosen on a theoretical basis. In this paper we propose several variants of a general structural factor forecasting model, and use these to forecast certain key macroeconomic variables. We make the choice of factors more structurally meaningful by estimating factors from subsets of information variables, where these variables can be assigned to subsets on the basis of economic theory. We compare the forecasting performance of the structural factor forecasting model with that of a univariate AR model, a standard VAR model, and some non-structural factor forecasting models. The results suggest that our structural factor forecasting model performs significantly better in forecasting real activity variables, especially at short horizons.     

Understanding models’ forecasting performance

We propose a new methodology to identify the sources of models’ forecasting performance. The methodology decomposes the models’ forecasting performance into asymptotically uncorrelated components that measure instabilities in the forecasting performance, predictive content, and over-fitting. The empirical application shows the usefulness of the new methodology for understanding the causes of the poor forecasting ability of economic models for exchange rate determination.     

组合预测模型在武汉港集装箱吞吐量预测中的应用

为使港口集装箱吞吐量预测的误差更小,精度更高,提出运用弹性系数法、灰色模型法、三次指数平滑法的组合预测模型,预测了武汉港未来特征年的集装箱吞吐量,研究结果表明,组合模型相比单一预测方法能够降低误差、提高精度,预测结果更加理想。     

组合负荷预测方法综述

电力系统负荷具有很多不确定因素,针对单一模型进行负荷预测时,预测精度不高这一问题,可采用组合预测法将多种预测方法所得的预测值进行加权平均而得到最终预测结果,以满足现代电力对负荷预测结果的准确性、快速性和智能化的要求。该文首先简要介绍了几种常用的负荷预测方法,接着详细介绍了组合负荷预测的研究现状及确定组合预测中各模型最优权重的几种方法,最后介绍了组合负荷预测模型的误差修正方法,对提高负荷预测的准确性有一定的现实意义。     

Differencing versus nondifferencing in factor-based forecasting

This paper studies performance of factor-based forecasts using differenced and nondifferenced data. Approximate variances of forecasting errors from the two forecasts are derived and compared. It is reported that the forecast using nondifferenced data tends to be more accurate than that using differenced data. This paper conducts simulations to compare root mean squared forecasting errors of the two competing forecasts. Simulation results indicate that forecasting using nondifferenced data performs better. The advantage of using nondifferenced data is more pronounced when a forecasting horizon is long and the number of factors is large. This paper applies the two competing forecasting methods to 68 I(1) monthly US macroeconomic variables across a range of forecasting horizons and sampling periods. We also provide detailed forecasting analysis on US inflation and industrial production. We find that forecasts using nondifferenced data tend to outperform those using differenced data.     

The performance of the global bottom-up approach in the M5 accuracy competition: A robustness check

The M5 accuracy competition has presented a large-scale hierarchical forecasting problem in a realistic grocery retail setting in order to evaluate an extended range of forecasting methods, particularly those adopting machine learning. The top ranking solutions adopted a global bottom-up approach, by which is meant using global forecasting methods to generate bottom level forecasts in the hierarchy and then using a bottom-up strategy to obtain coherent forecasts for aggregate levels. However, whether the observed superior performance of the global bottom-up approach is robust over various test periods or only an accidental result, is an important question for retail forecasting researchers and practitioners. We conduct experiments to explore the robustness of the global bottom-up approach, and make comments on the efforts made by the top-ranking teams to improve the core approach. We find that the top-ranking global bottom-up approaches lack robustness across time periods in the M5 data. This inconsistent performance makes the M5 final rankings somewhat of a lottery. In future forecasting competitions, we suggest the use of multiple rolling test sets to evaluate the forecasting performance in order to reward robustly performing forecasting methods, a much needed characteristic in any application.     

An unobserved component model for multi-rate forecasting of telephone call demand: the design of a forecasting support system

An Unobserved Components (UC) Model based on an enhanced version of the Dynamic Harmonic Regression model, including new multi-rate and modulated cycle procedures, is used to develop a customised package for forecasting and signal extraction applied to hourly telephone call numbers made to Barclaycard plc. service centres, with a forecasting horizon of up to several weeks in advance. The paper outlines both the methodological and algorithmic aspects of the modelling, forecasting and signal extraction procedures, including the design and implementation of forecasting support software with a specially designed Graphical User Interface within the ^® computing environment. The forecasting performance is evaluated comprehensively in comparison with the well-known seasonal ARIMA approach.     

Conducting a sales forecasting audit

Continuous improvement in sales forecasting is a worthy goal for any organization. This paper describes a methodology for conducting a sales forecasting audit, the goal of which is to help a company understand the status of its sales forecasting processes and identify ways to improve those processes. The methodology described here has been developed over a 5-year period, involving multiple auditors, and has been implemented (to date) at 16 organizations. This methodology revolves around three distinct phases: the ‘as-is’ phase, in which the audit team seeks to understand fully a company’s current forecasting process; the ‘should-be’ phase, in which the audit team presents a vision of what world-class forecasting should look like at the audited company, and the ‘way-forward’ phase, in which the audit team presents a roadmap of how the company can change its forecasting processes to achieve world-class levels. Those companies that have responded positively to the audit process have experienced significant improvement in their forecasting performance. The paper concludes by presenting lessons from audits conducted to date, as well as implications for management practice and future research.     

Simple averaging of direct and recursive forecasts via partial pooling using machine learning

This article introduces the winning method at the M5 Accuracy competition. The presented method takes a simple manner of averaging the results of multiple base forecasting models that have been constructed via partial pooling of multi-level data. All base forecasting models of adopting direct or recursive multi-step forecasting methods are trained by the machine learning technique, LightGBM, from three different levels of data pools. At the competition, the simple averaging of the multiple direct and recursive forecasting models, called DRFAM, obtained the complementary effects between direct and recursive multi-step forecasting of the multi-level product sales to improve the accuracy and the robustness.     

Forecasting systems in managing hospital services demand: a review of applicability and a measure of utility.

A comprehensive literature review of forecasting methodologies and their specific applications to managing hospital services demand provided a credible base for the ensuing study of current forecasting usage. A sample of 40 hospitals was analyzed to measure the current perceived urgency to utilize forecasting systems. These findings were then compared with perceived actual usage. The incidence of formal forecasting systems actually being utilized was lower than the perceived need to use such systems. Identification of principal methodologies utilized and an assessment of computer-assisted forecasting indicated that a strong reliance on qualitative, manually-derived methodologies still remains. Correlation analyses of key exogenous variables indicated that the larger sized hospitals utilized computerized methodologies and had the highest measures of perceived need for, and actual practice of, formal forecasting programs.     

Combining wavelet decomposition with machine learning to forecast gold returns

This paper combines the discrete wavelet transform with support vector regression for forecasting gold-price dynamics. The advantages of this approach are investigated using a relatively small set of economic and financial predictors. I measure model performance by differentiating between a statistically-motivated out-of-sample forecasting exercise and an economically-motivated trading strategy. Disentangling the predictors with respect to their time and frequency domains leads to improved forecasting performance. The results are robust compared to alternative forecasting approaches. My findings on the relative importances of such wavelet decompositions suggest that the influences of short-term and long-term trends are not stable over the full evaluation period.     

基于时间序列的支持向量机在物流预测中的应用

由于物流预测是不确定的、非线性的、动态开放性的复杂大系统，传统方法往往难以准确地描述这种复杂的非线性特征，因而无法准确进行物流预测，本文提出了基于一种基于时间序列的支持向量机(SVM)的物流预测方法。将该方法用于实际物流系统的公路运输量预测中，和真实值比较说明所提出的物流预测方法是可行和有效的。