Value-added forecasting

Long ago, the emphasis shifted away from forecasting as a competitive weapon when it became apparent that forecast error could never be eliminated. Forecasts became a necessary evil that no one wanted to claim responsibility for. It's time to clear up some of the misconceptions about forecasts and to seize the opportunity inherent in the forecasting process. It is not forecast accuracy but rather improved understanding and use of forecasting as a tool for reducing both costs and lead times that will add real value to an enterprise and can improve the results from any and all other initiatives.     

The impact of institutional change on forecast accuracy: A case study of budget forecasting in Washington State

This paper explores the relationship between institutional change and forecast accuracy via an analysis of the entitlement caseload forecasting process in Washington State. This research extends the politics of forecasting literature beyond the current area of government revenue forecasting to include expenditure forecasting and introduces an in-depth longitudinal study to the existing set of cross-sectional studies. Employing a fixed-effects model and ordinary least squares regression analysis, this paper concludes that the establishment of an independent forecasting agency and subsequent formation of technical workgroups improve forecast accuracy. Additionally, this study finds that more frequent forecast revisions and structured domain knowledge improve forecast accuracy.     

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。     

现代物流中的销售预测模型研究

针对销售物流对销售预测的要求，尝试采用改进了的适应指数平滑法，建立短期销售预测模型，并对模型进行验证，同时采用客户调查法和销售物流人员综合意见法，进行同样目的的预测，将各种预测方法得出的结果进行融合，得到最终预测结果。     

组合预测方法研究综述

相较于传统预测方法,组合预测在预测精度以及信息的利用上得到了很大的提高。文章将组合预测分为了线性与非线性两类,总结了5种主要的组合预测模型以及各种组合系数的计算方法,分析了组合预测未来的发展趋势。     

Economic, organizational, and political influences on biases in forecasting state sales tax receipts

This paper investigates factors influencing fixed bias in forecasting state sales taxes revenues. By extending an existing model used to explain forecast accuracy to include a series of complex interactions related to the potential political and policy use of revenue forecasts, the paper extends our understanding of the forecasting process in government. Exploratory empirical analysis based on survey data is used to provide evidence that bias in forecasting results, at least in part, from political and policy manipulation. There is also evidence that institutional reforms associated with ‘good management’ practices affect forecast bias, but in complex ways depending upon the extent to which political competition exists within the state.     

How does improved forecasting benefit detection? An application to biosurveillance

While many methods have been proposed for detecting disease outbreaks from pre-diagnostic data, their performance is usually not well understood. We argue that most existing temporal detection methods for biosurveillance can be characterized as a forecasting component coupled with a monitoring/detection component.In this paper, we describe the effect of forecast accuracy on detection performance. Quantifying this effect allows one to measure the benefits of improved forecasting and determine when it is worth improving a forecast method’s precision at the cost of robustness or simplicity. We quantify the effect of forecast accuracy on detection metrics under different scenarios and investigate the effect when standard assumptions are violated. We illustrate our results by examining performance on authentic biosurveillance data.     

Forecasting Errors: the Track Record

This Briefing Paper is the last of a series of three about forecasting. In this one we examine our forecasting record; it complements the February paper in which we analysed the properties of our forecasting model in terms of the error bands attached to the central forecast.
There are many ways of measuring forecasting errors, and in the first part of this Briefing Paper we describe briefing how we have tackled the problem. (A more detailed analysis can be found in the Appendix.) In Part II we report and comment upon the errors in our forecasts of annual growth rates and show how our forecasting performance has improved over the years. In Part III we focus on quarterly forecasts up to 8 quarters ahead, and compare our forecasting errors with measurement errors in the oficial statistics; with the estimation errors built into our forecast equations; and with the stochastic model errors we reported last February. A brief summary of the main conclusions is given below.     

考虑市场力的短期边际电价预测

电价波动较负荷波动剧烈,使得整个电价的预测精度降低。造成这种价格波动的主要原因是由于在电力市场中,发电商拥有的市场力具有能够支配电价上下波动的能力,使得电价的变化更加难以预测。因此市场力在电价预测中是必须考虑的重要因素之一。提出将市场供需比指标作为电价预测的一个输入量,将其引入到预测模型中作为影响电价的因素,使预测精度得到提高。     

Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN

Traditional sales forecasting methods are mainly based on historical sales data, which result in a certain lag. The relationship between sales volume and its influencing factors is intricate and often non-linear. In view of this, we propose a novel product forecasting method using online reviews and search engine data. Firstly, a dictionary-based sentiment analysis method is developed to convert the textual review concerning each attribute of the product into the corresponding sentiment score. And by combining the prospect theory and relevant online review data, sentiment indices in each period are calculated. Subsequently, data of product-related Baidu search words with different lag orders are collected and screened by time difference correlation analysis. Finally, the forecast model, PCA–DSFOA–BPNN, is constructed by combining the principal component analysis (PCA), the back propagation neural network (BPNN), and the improved fruit fly optimization algorithm (DSFOA), in which sentiment indices, Baidu search data, and historical sales volume are input data. Taking the monthly sales forecast of 14 automobile models as a case study, we observe that the proposed forecast method can effectively improve the forecast accuracy with good robustness.     

A new approach to estimating earnings forecasting models: Robust regression MM-estimation

This study focuses on the impact of model estimation methods on earnings forecast accuracy. Compared with an ordinary least squares (OLS) regression combined with winsorization, robust regression MM-estimation improves the earnings forecast accuracy of all the models examined, especially for those with more variables. My findings indicate that the impact of outliers on the OLS regression increases with the number of variables in the models, alerting researchers who use OLS regressions for forecasting. My findings explain the puzzling negative relationship between earnings forecast accuracy and the number of model variables in prior research. Moreover, I demonstrate the valuation implications of earnings forecasted using robust regression MM-estimation. This study contributes to earnings forecasting, valuation, and influential observation treatment in forecasting.     

FFORMA: Feature-based forecast model averaging

We propose an automated method for obtaining weighted forecast combinations using time series features. The proposed approach involves two phases. First, we use a collection of time series to train a meta-model for assigning weights to various possible forecasting methods with the goal of minimizing the average forecasting loss obtained from a weighted forecast combination. The inputs to the meta-model are features that are extracted from each series. Then, in the second phase, we forecast new series using a weighted forecast combination, where the weights are obtained from our previously trained meta-model. Our method outperforms a simple forecast combination, as well as all of the most popular individual methods in the time series forecasting literature. The approach achieved second position in the M4 competition.     

Real-time squared: A real-time data set for real-time GDP forecasting

This paper uses real-time data to mimic real-time GDP forecasting activity. Through automatic searches for the best indicators for predicting GDP one and four steps ahead, we compare the out-of-sample forecasting performance of adaptive models using different data vintages, and produce three main findings. First, despite data revisions, the forecasting performance of models with indicators is better, but this advantage tends to vanish over longer forecasting horizons. Second, the practice of using fully updated datasets at the time the forecast is made (i.e., taking the best available measures of today's economic situation) does not appear to bring any effective improvement in forecasting ability: the first GDP release is predicted equally well by models using real-time data as by models using the latest available data. Third, although the first release is a rational forecast of GDP data after all statistical revisions have taken place, the forecast based on the latest available GDP data (i.e. the “temporarily best” measures) may be improved by combining preliminary official releases with one-step-ahead forecasts.     

Probabilistic forecasting of heterogeneous consumer transaction–sales time series

We present new Bayesian methodology for consumer sales forecasting. Focusing on the multi-step-ahead forecasting of daily sales of many supermarket items, we adapt dynamic count mixture models for forecasting individual customer transactions, and introduce novel dynamic binary cascade models for predicting counts of items per transaction. These transaction–sales models can incorporate time-varying trends, seasonality, price, promotion, random effects and other outlet-specific predictors for individual items. Sequential Bayesian analysis involves fast, parallel filtering on sets of decoupled items, and is adaptable across items that may exhibit widely-varying characteristics. A multi-scale approach enables information to be shared across items with related patterns over time in order to improve prediction, while maintaining the scalability to many items. A motivating case study in many-item, multi-period, multi-step-ahead supermarket sales forecasting provides examples that demonstrate an improved forecast accuracy on multiple metrics, and illustrates the benefits of full probabilistic models for forecast accuracy evaluation and comparison.     

Forecasting welfare caseloads: The case of the Japanese public assistance program

Forecasting welfare caseloads has grown in importance in Japan because of their recent rapid increase. Given that the forecasting literature on welfare caseloads only focuses on US cases and utilizes limited classes of forecasting models, this study employs multiple alternative methods in order to forecast Japanese welfare caseloads and compare forecasting performances. In pseudo real-time forecasting, VAR and forecast combinations tend to outperform the other methods investigated. In real-time forecasting, however, a simple version of forecast combinations seems to perform better than the remaining models, predicting that welfare caseloads in Japan will surpass 1.7 million by the beginning of 2016, an approximately 20% increase in five years from the beginning of 2011.     

Mismatch and the Forecasting Performance of Matching Functions

This paper investigates the role of structural imbalance between job seekers and job openings for the forecasting performance of a labour market matching function. Starting from a Cobb–Douglas matching function with constant returns to scale (CRS) in each frictional micro market shows that on the aggregate level, a measure of mismatch is a crucial ingredient of the matching function and hence should not be ignored for forecasting hiring figures. Consequently, we allow the matching process to depend on the level of regional, qualificatory and occupational mismatch between unemployed and vacancies. In pseudo out‐of‐sample tests that account for the nested model environment, we find that forecasting models enhanced by a measure of mismatch significantly outperform their benchmark counterparts for all forecast horizons ranging between one month and a year. This is especially pronounced during and in the aftermath of the Great Recession where a low level of mismatch improved the possibility of unemployed to find a job again. The results show that imposing CRS helps improve forecast accuracy compared to unrestricted models.     

A combined random effect and fixed effect forecast for panel data models

When some of the regressors in a panel data model are correlated with the random individual effects, the random effect (RE) estimator becomes inconsistent while the fixed effect (FE) estimator is consistent. Depending on the various degree of such correlation, we can combine the RE estimator and FE estimator to form a combined estimator which can be better than each of the FE and RE estimators. In this paper, we are interested in whether the combined estimator may be used to form a combined forecast to improve upon the RE forecast (forecast made using the RE estimator) and the FE forecast (forecast using the FE estimator) in out-of-sample forecasting. Our simulation experiment shows that the combined forecast does dominate the FE forecast for all degrees of endogeneity in terms of mean squared forecast errors (MSFE), demonstrating that the theoretical results of the risk dominance for the in-sample estimation carry over to the out-of-sample forecasting. It also shows that the combined forecast can reduce MSFE relative to the RE forecast for moderate to large degrees of endogeneity and for large degrees of heterogeneity in individual effects.     

The tourism forecasting competition

We evaluate the performances of various methods for forecasting tourism data. The data used include 366 monthly series, 427 quarterly series and 518 annual series, all supplied to us by either tourism bodies or academics who had used them in previous tourism forecasting studies. The forecasting methods implemented in the competition are univariate and multivariate time series approaches, and econometric models. This forecasting competition differs from previous competitions in several ways: (i) we concentrate on tourism data only; (ii) we include approaches with explanatory variables; (iii) we evaluate the forecast interval coverage as well as the point forecast accuracy; (iv) we observe the effect of temporal aggregation on the forecasting accuracy; and (v) we consider the mean absolute scaled error as an alternative forecasting accuracy measure. We find that pure time series approaches provide more accurate forecasts for tourism data than models with explanatory variables. For seasonal data we implement three fully automated pure time series algorithms that generate accurate point forecasts, and two of these also produce forecast coverage probabilities which are satisfactorily close to the nominal rates. For annual data we find that Naïve forecasts are hard to beat.     

Stability in the inefficient use of forecasting systems: A case study in a supply chain company

Computer-based demand forecasting systems have been widely adopted in supply chain companies, but little research has studied how these systems are actually used in the forecasting process. We report the findings of a case study of demand forecasting in a pharmaceutical company over a 15-year period. At the start of the study, managers believed that they were making extensive use of their forecasting system that was marketed based on the accuracy of its advanced statistical methods. Yet most forecasts were obtained using the system’s facility for judgmentally overriding the automatic statistical forecasts. Carrying out the judgmental interventions involved considerable management effort as part of a sales & operations planning (S&OP) process, yet these often only served to reduce forecast accuracy. This study uses observations of the forecasting process, interviews with participants and data on the accuracy of forecasts to investigate why the managers continued to use non-normative forecasting practices for many years despite the potential economic benefits that could be achieved through change. The reasons for the longevity of these practices are examined both from the perspective of the individual forecaster and the organization as a whole.     

Combination of long term and short term forecasts, with application to tourism demand forecasting

Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time aggregations. For example, we could create a yearly time series from a monthly time series and produce forecasts for both, then combine the forecasts. These forecasts would each be tracking the dynamics of different time scales, and would therefore add diverse types of information. A comparison of several forecast combination methods, performed in the context of this setup, shows that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined.As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we consider 33 individual source countries, as well as the aggregate. The novel combination strategy also produces a generally improved forecasting accuracy.