Forecasting: A hybrid approach

It has recently been shown that the Box-Jenkins approach to forecasting time series is superior to an econometric approach over a relatively short horizon. The results here support this contention. A combination of the two approaches, however, proves to be clearly superior to either one separately. By taking account of changes in economic and weather related variables in a time series model, improved forecasts are obtained.     

The Impact of External Demand Information on Parallel Supply Chains with Interacting Demand

This paper considers two parallel supply chains with interacting demand streams. Each supply chain consists of one supplier and one retailer. The two demand streams are jointly described with a vector autoregressive time‐series process in which they interact and their respective innovation errors correlate contemporaneously. For each supply chain, we develop insights into when and how much the supplier and the retailer can improve on their forecasting accuracy if the external demand history of the other supply chain is utilized. When this external demand history is not available or made available after a time lag, we develop a partial process and a delayed process to characterize the demand structure that the retailer can recover from the available demand histories. Our results show that the external demand history of the other supply chain always helps the retailer make better forecasts when demand streams interact; however, the enhanced information alters the retailer's order process, which may produce larger forecasting errors for the supplier. Conditions are established for the supplier to benefit from the external demand history of the other supply chain.     

Some Empirical Findings on Short-Term Forecasting: Technique Complexity and Combinations

The purpose of this research is to determine if prior findings that favor simple forecasting techniques and technique combinations hold true in a short-term forecasting environment, where demand data can be quite volatile. Twenty-two time series of daily data from a real business setting are used to test one-period ahead forecasts, the epitome of short-term forecasting. The time series vary systematically as to data volatility and forecast difficulty. Forecast accuracy is measured in terms of both mean absolute percentage error (MAPE) and mean percentage error (MPE).     

COMPARING FORECAST ACCURACY FOR EXPONENTIAL SMOOTHING MODELS OF EARNINGS-PER-SHARE DATA FOR FINANCIAL DECISION MAKING

This paper relates recent research in predicting accounting earnings per share (EPS) to an experiment comparing the performance of extrapolative forecasting models. The paper points out the usefulness of the results to decision-making processes such as those used in portfolio analysis or financial management. The statistical results of the experiment point to the usefulness of the Holt-Winter (HW) model in predicting EPS for a random sample of firms over a 20-year horizon. For short-term forecasting, the HW model provides relatively accurate forecasts in comparison to other methods used. HW is likely to be a costeffective alternative to more time-consuming and expensive techniques.     

基于修剪平均的神经网络集成时序预测方法

由于复杂时序存在结构性断点和异常值等问题，往往导致预测模型训练效果不佳，并可能出现极端预测值的情况。为此，本文提出了基于修剪平均的神经网络集成预测方法。该方法首先从训练数据中生成多组训练集，然后分别训练多个神经网络预测模型，最后将多个神经网络的预测结果使用修剪平均策略进行集成。相较于简单平均策略而言，修剪平均策略不容易受到极值的影响，能够使集成模型获得鲁棒性强的预测效果。在实证研究中，本文构造了两种神经网络集成预测模型，分别为基于修剪平均的自举神经网络集成模型(Trimmed Average based Bootstrap Neural Network Ensemble, TA-BNNE)和基于修剪平均的蒙特卡洛神经网络集成模型(Trimmed Average based Monte Carlo Neural Network Ensemble, TA-MCNNE)，并采用这两种模型对NN3竞赛数据集进行预测，结果表明在常规和复杂数据集上，修剪平均策略比简单平均策略具有更好的预测精度。此外，本文将所提出的集成模型与NN3的前十名模型进行比较，发现两种模型在全部数据集上均超过了第6名，在复杂数据集上的表现均超过了第1名，进一步验证本文所提方法的有效性。     

Forecasting industrial production using structural time series models

Industrial production data series are volatile and often also cyclical. Hence, univariate time series models which allow for these features are expected to generate relatively accurate forecasts of industrial production. A particular class of unobservable components models — structural time series models — is used to generate forecasts of Austrian and German industrial production. A widely applied ARIMA model is used as a baseline for comparison. The empirical results show that the basic structural model generates more accurate forecasts than the ARIMA model when accuracy is measured in terms of size of error or directional change; and that the basic structural model forecasts better than the structural model with a cyclical component included on the basis of numerical measures, and tracking error for month-to-month changes.     

The choice of a forecasting model

The major purpose of studies of forecasting accuracy is to help forecasters select the ‘best’ forecasting method. This paper examines accuracy studies in particular that of Makridakis et al. [20] with a view to establishing how they contribute to model choice. It is concluded that they affect the screening that most forecasters go through in selecting a range of methods to analyze—in Bayesian terms they are a major determinant of ‘prior knowledge’. This general conclusion is illustrated in the specific case of the Makridakis Competition (M-Competition). A survey of expert forecasters was made in both the UK and US. The respondents were asked about their familiarity with eight methods of univariate time series forecasting, and their perceived accuracy in three different forecasting situations. The results, similar for both the UK and US, were that the forecasters were relatively familiar with all the techniques included except Holt-Winters and Bayesian. For short horizons Box-Jenkins was seen as most accurate while trend curves was perceived as most suitable for the long horizons. These results are contrasted with those of the M-Competition, and conclusions drawn on how the results of the M-Competition should influence model screening and model choice.     

Combining Judgmental and Statistical Forecasts: An Application to Earnings Forecasts

This study investigated the accuracy of combinations of statistical and judgmental forecasts of annual accounting earnings. Combined forecasts were generated as equally weighted (i.e., simple averages) and unequally weighted combinations of individual forecasts from time-series models of quarterly and annual earnings (statistical forecasts) and security analysts' forecasts of quarterly and annual earnings (judgmental forecasts). The effect of the number of individual forecasts combined on the accuracy of the combined forecasts was also examined. The empirical results indicated that, on the average, combined forecasts were more accurate than individual forecasts. The results also indicated that although analysts' forecasts are based on a wider information set, the accuracy of their forecasts could be improved by combining them with forecasts generated from statistical models. Even if the best individual forecast could be identified in advance, gains in accuracy could be achieved by using combinations of two other forecasting methods. Several of the combined forecasts were superior to the most accurate individual forecast. Forecasts combined by using unequal weights derived from a regression model proved more accurate than equally weighted combinations. Forecasting accuracy improved and the variability of accuracy across different combinations decreased as the number of forecasts in the combination increased.     

汇率的非线性组合预测方法研究

近年来的经济统计研究表明,组合预测比单项预测具有更高的预测精度,但线性组合预测方法在汇率的组合建模与预测方面存在着较大的局限性。本文提出了一种基于模糊神经网络的汇率非线性组合建模与预测新方法,并给出了相应的混合学习算法。对于英镑、法朗、瑞士法朗、日本元对美元等汇率时间序列的组合建模与预测结果表明,该方法具有很强的学习与泛化能力,在处理外汇市场这种具有一定程度不确定性的非线性系统的组合建模与预测方面有很好的应用价值。     

Neural Network Earnings per Share Forecasting Models: A Comparative Analysis of Alternative Methods

In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate linear models that incorporate fundamental accounting variables (i.e., inventory, accounts receivable, and so on) with the forecast accuracy of neural network models. Unique to this study is the focus of our comparison on the multivariate models to examine whether the neural network models incorporating the fundamental accounting variables can generate more accurate forecasts of future earnings than the models assuming a linear combination of these same variables. We investigate four types of models: univariate‐linear, multivariate‐linear, univariate‐neural network, and multivariate‐neural network using a sample of 283 firms spanning 41 industries. This study shows that the application of the neural network approach incorporating fundamental accounting variables results in forecasts that are more accurate than linear forecasting models. The results also reveal limitations of the forecasting capacity of investors in the security market when compared to neural network models.     

Improving the accuracy and operational predictability of project cost forecasts: an adaptive combination approach

Conducting an early warning forecast to detect potential cost overrun is essential for timely and effective decision-making in project control. This paper presents a forecast combination model that adaptively identifies the best forecast and optimises various combinations of commonly used project cost forecasting models. To do so, a forecast error simulator is formulated to visualise and quantify likely error profiles of forecast models and their combinations. The adaptive cost combination (ACC) model was applied to a pilot project for numerical illustration as well as to real world projects for practical implementation. The results provide three valuable insights into more effective project control and forecasting. First, the best forecasting model may change in individual projects according to the project progress and the management priority (i.e. accuracy, outperformance or large errors). Second, adaptive combination of simple, index-based forecasts tends to improve forecast accuracy, while mitigating the risk of large errors. Third, a post-mortem analysis of seven real projects indicated that the simple average of two most commonly used cost forecasts can be 31.2% more accurate, on average, than the most accurate alternative forecasts.     

Application of an MRP matrix-based hierarchical planning model to a furniture company

This paper is concerned with the definition of a feasible master schedule for operations management, obtained through an integrated planning system, using a hierarchical methodology (by means of different disaggregation stages) with an appropriate time horizon for connection with the manufacturing requirements planning unit (MRP II). The hierarchical model obtained considers not only product aggregation into types, families and items but also resources aggregation, structure of products aggregation or bill-of-materials aggregation, as well as temporary aggregation. It allows the creation of a master schedule for an adequate time horizon, that can be used as an     

A new product growth model with a dynamic market potential

Traditional new product diffusion models have assumed a constant market potential over the planning horizon for forecasting product adoptions. This assumption is conceptually unsound and is likely to yield either theoretically unacceptable parameter estimates of the model or poor demand forecasts. This paper presents a dynamic growth model which relaxes this assumption. Model illustrations, limitations and further extensions are included.     

Sales Forecasting with Financial Indicators and Experts' Input

We present a method for forecasting sales using financial market information and test this method on annual data for US public retailers. Our method is motivated by the permanent income hypothesis in economics, which states that the amount of consumer spending and the mix of spending between discretionary and necessity items depend on the returns achieved on equity portfolios held by consumers. Taking as input forecasts from other sources, such as equity analysts or time‐series models, we construct a market‐based forecast by augmenting the input forecast with one additional variable, lagged return on an aggregate financial market index. For this, we develop and estimate a martingale model of joint evolution of sales forecasts and the market index. We show that the market‐based forecast achieves an average 15% reduction in mean absolute percentage error compared with forecasts given by equity analysts at the same time instant on out‐of‐sample data. We extensively analyze the performance improvement using alternative model specifications and statistics. We also show that equity analysts do not incorporate lagged financial market returns in their forecasts. Our model yields correlation coefficients between retail sales and market returns for all firms in the data set. Besides forecasting, these results can be applied in risk management and hedging.     

短期电价预测的组合混沌方法

电力市场中,电价的变化呈现的是一种非线性的、动态开放的过程,传统的方法已很难提高其预测精度。为此,本文提出一种基于小波变换、计量经济学模型和径向基函数网络的组合混沌预测方法。首先利用小波变换将原电价序列分解、重构成概貌序列和细节序列;在此基础上,针对不同的子序列建立不同的模型,并进行预测;最后将所有子序列的预测结果求和,作为最终的预测值。对西班牙电力市场短期电价的预测表明,该方法具有很高的预测精度。     

The futility of forecasting

Long-range forecasting is an integral part of planning, but relying on its accuracy may be a mistake. The landscape is strewn with often wildly inaccurate forecasts. This article studies performances of some forecasts, analyses factors contributing to forecast error, and suggests ways in which management may deal with the uncertainty resulting from faulty forecasting performances.     

Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis

Intermittent demand is characterized by occasional demand arrivals interspersed by time intervals during which no demand occurs. These demand patterns pose considerable difficulties in terms of forecasting and stock control due to their compound nature, which implies variability both in terms of demand arrivals and demand sizes. An intuitively appealing strategy to deal with such patterns from a forecasting and stock control perspective is to aggregate demand in lower-frequency ‘time buckets’, thereby reducing the presence of zero observations. In this paper, we investigate the impact of forecasting aggregation on the stock control performance of intermittent demand patterns. The benefit of the forecasting aggregation approach is empirically assessed by means of analysis on a large demand dataset from the Royal Air Force (UK). The results show that the aggregation forecasting approach results in higher achieved service levels as compared to the classical forecasting approach. Moreover, when the combined service-cost performance is considered, the results also show that the former approach is more efficient than the latter, especially for high target service levels.     

Managing Functional Biases in Organizational Forecasts: A Case Study of Consensus Forecasting in Supply Chain Planning

To date, little research has been done on managing the organizational and political dimensions of generating and improving forecasts in corporate settings. We examine the implementation of a supply chain planning process at a consumer electronics company, concentrating on the forecasting approach around which the process revolves. Our analysis focuses on the forecasting process and how it mediates and accommodates the functional biases that can impair the forecast accuracy. We categorize the sources of functional bias into intentional, driven by misalignment of incentives and the disposition of power within the organization, and unintentional, resulting from informational and procedural blind spots. We show that the forecasting process, together with the supporting mechanisms of information exchange and elicitation of assumptions, is capable of managing the potential political conflict and the informational and procedural shortcomings. We also show that the creation of an independent group responsible for managing the forecasting process, an approach that we distinguish from generating forecasts directly, can stabilize the political dimension sufficiently to enable process improvement to be steered. Finally, we find that while a coordination system—the relevant processes, roles and responsibilities, and structure—can be designed to address existing individual and functional biases in the organization, the new coordination system will in turn generate new individual and functional biases. The introduced framework of functional biases (whether those biases are intentional or not), the analysis of the political dimension of the forecasting process, and the idea of a coordination system are new constructs to better understand the interface between operations management and other functions.     

A COMPARISON OF LEARNING CURVE ANALYSIS AND MOVING AVERAGE RATIO ANALYSIS FOR DETAILED OPERATIONAL PLANNING*

This study tests the use of learning curve analysis for production planning at the detailed component level under various conditions, represented by factors of product turnover rate, learning rate, variance levels, and planning horizon length. It also presents an alternative to learning curve analysis that considers aggregation of cost data across time. This alternative is periodic revision of standard cost data using moving average forecasts to reflect productivity trends. Results of this study indicate that in most circumstances a moving average analysis can provide better estimates of short-term, detailed component operations costs than either a learning curve analysis or a standard analysis.