Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations

This paper examines the feasibility of rule -based forecasting, a procedure that applies forecasting expertise and domain knowledge to produce forecasts according to features of the data. We developed a rule base to make annual extrapolation forecasts for economic and demographic time series. The development of the rule base drew upon protocol analyses of five experts on forecasting methods. This rule base, consisting of 99 rules, combined forecasts from four extrapolation methods (the random walk, regression, Brown's linear exponential smoothing, and Holt's exponential smoothing) according to rules using 18 features of time series. For one-year ahead ex ante forecasts of 90 annual series, the median absolute percentage error (MdAPE) for rule- based forecasting was 13% less than that from equally-weighted combined forecasts. For six-year ahead ex ante forecasts, rule-based forecasting had a MdAPE that was 42% less. The improvement in accuracy of the rule - based forecasts over equally-weighted combined forecasts was statistically significant. Rule-based forecasting was more accurate than equal-weights combining in situations involving significant trends, low uncertainty, stability, and good domain expertise.Rule-based forecasting, time series

The Profitability of Winning

Sports and war metaphors abound in business today. For example, one management book, Thunder in the Sky, by Thomas Cleary, opens with a Chinese saying that translates: “The marketplace is a battlefield. The Asian people view success in the business world as tantamount to victory in battle.” The book advises American executives to do the same. However, such metaphors are misleading. The objective in both sports and war is to beat the competitor. Business, on the other hand, aims to create wealth.business, profits, winning,

Integration of Statistical Methods and Judgment for Time Series

We consider how judgment and statistical methods should be integrated for time-series forecasting. Our review of published empirical research identified 47 studies, all but four published since 1985. Five procedures were identified: revising judgment; combining forecasts; revising extrapolations; rule-based forecasting; and econometric forecasting. This literature suggests that integration generally improves accuracy when the experts have domain knowledge and when significant trends are involved. Integration is valuable to the extent that judgments are used as inputs to the statistical methods, that they contain additional relevant information, and that the integration scheme is well structured. The choice of an integration approach can have a substantial impact on the accuracy of the resulting forecasts. Integration harms accuracy when judgment is biased or its use is unstructured. Equal-weights combining should be regarded as the benchmark and it is especially appropriate where series have high uncertainty or high instability. When the historical data involve high uncertainty or high instability, we recommend revising judgment, revising extrapolations, or combining. When good domain knowledge is available for the future as well as for the past, we recommend rule- based forecasting or econometric methods.statistical methods, statistics, time series, forecasting, empirical research

Causal Forces: Structuring Knowledge for Time-series Extrapolation

This paper examines a strategy for structuring one type of domain knowledge for use in extrapolation. It does so by representing information about causality and using this domain knowledge to select and combine forecasts. We use five categories to express causal impacts upon trends: growth, decay, supporting, opposing, and regressing. An identification of causal forces aided in the determination of weights for combining extrapolation forecasts. These weights improved average ex ante forecast accuracy when tested on 104 annual economic and demographic time series. Gains in accuracy were greatest when (1) the causal forces were clearly specified and (2) stronger causal effects were expected, as in longer- range forecasts. One rule suggested by this analysis was: “Do not extrapolate trends if they are contrary to causal forces.” We tested this rule by comparing forecasts from a method that implicitly assumes supporting trends (Holt’s exponential smoothing) with forecasts from the random walk. Use of the rule improved accuracy for 20 series where the trends were contrary; the MdAPE (Median Absolute Percentage Error) was 18% less for the random walk on 20 one-year ahead forecasts and 40% less for 20 six-year-ahead forecasts. We then applied the rule to four other data sets. Here, the MdAPE for the random walk forecasts was 17% less than Holt’s error for 943 short-range forecasts and 43% less for 723 long-range forecasts. Our study suggests that the causal assumptions implicit in traditional extrapolation methods are inappropriate for many applications.Causal forces Combining Contrary trends Damped trends Exponential smoothing Judgment Rule-based forecasting Selecting methods

Alien Registration- Armstrong, Fred (Lagrange, Penobscot County)

https://digitalmaine.com/alien_docs/7743/thumbnail.jp

Generalization and Communication Issues in the Use of Error Measures: A Reply

Generalization and communication issues in the use o f error measures: A reply, Fred Collopy, The Weatherhead School, Case-Western Reserve University, Cleveland, Ohio 44118, USA and J. Scott Armstrong, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA. We agree with most of what the commentators say about Armstrong and Collopy (1992), hereafter referred to as “AC,” and Fildes (1992), hereafter referred to as “F.” Here, we address three issues where we do not agree entirely: (1) Can the results from the M-competition be generalized? (2) Is Theil\u27s U2 easy to communicate? (3) Would a richer set of measures lead to improvements in the selection and development of forecasting methods? Our own answers to these questions are “yes,” “no,” and “probably not,” respectively

The profitability of winning

Introduction: Sports and war metaphors abound in business today. For example, one management book, Thunder in the Sky, by Thomas Cleary, opens with a Chinese saying that translates: “The marketplace is a battlefield. The Asian people view success in the business world as tantamount to victory in battle.” The book advises American executives to do the same. However, such metaphors are misleading. The objective in both sports and war is to beat the competitor. Business, on the other hand, aims to create wealth. Ignoring this reality, many people believe the metaphors and choose competitor-oriented strategies that pursue market share rather than profits, according to research at Wharton, Weatherhead, and other institutions. In one study, one-third of 105 managers said their firms had competitor-oriented objectives. Another revealed that managers opted to sacrifice profits to beat the competition on price. A third showed that firms stating pricing goals in competitive terms had lower returns-on-investment over a 45-year span. Our conclusion: Firms should focus on profits, not competition. Our premise is neither mysterious nor modern. It originates in classical micro-economics. Competitive objectives are especially harmful, because they likely will provoke unfavorable reactions from business rivals, which may, in turn, lead to price wars

Toward computer-aided forecasting systems: gathering, coding, and validating the knowledge

Direct assessment and protocol analysis were used to examine the processes that experts employ to make forecasts. The sessions with the experts yielded rules about when various extrapolation methods are likely to be most useful in obtaining accurate forecasts. The use of a computer-aided protocol analysis resulted in a reduction in the total lime required to code an expert\u27s knowledge. The implications for overcoming the knowledge acquisition bottleneck are considered

Expert Opinions About Extrapolation and the Mystery of the Overlooked Discontinuities

We report on the opinions of 49 forecasting experts on guidelines for extrapolation methods. They agreed that seasonality, trend, aggregation, and discontinuities were key features to use for selecting extrapolation methods. The strong agreement about the importance of discontinuities was surprising because this topic has been largely ignored in the forecasting literature

Rule-based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations

This paper examines the feasibility of rule-based forecasting, a procedure that applies forecasting expertise and domain knowledge to produce forecasts according to features of the data. We developed a rule base to make annual extrapolation forecasts for economic and demographic time series. The development of the rule base drew upon protocol analyses of five experts on forecasting methods. This rule base, consisting of 99 rules, combined forecasts from four extrapolation methods (the random walk, regression, Brown\u27s linear exponential smoothing, and Holt\u27s exponential smoothing) according to rules using 18 features of time series. For one-year ahead ex ante forecasts of 90 annual series, the median absolute percentage error (MdAPE) for rule-based forecasting was 13% less than that from equally-weighted combined forecasts. For six-year ahead ex ante forecasts, rule-based forecasting had a MdAPE that was 42% less. The improvement in accuracy of the rule-based forecasts over equally-weighted combined forecasts was statistically significant. Rule-based forecasting was more accurate than equal-weights combining in situations involving significant trends, low uncertainty, stability, and good domain expertise