On the use of multi-step cost functions for generating forecasts Article Swipe
Accurate forecasts are of principal importance for operations. Exponential smoothing is widely used due to its simplicity, relatively good forecast accuracy, ease of implementation and automation. The literature has continuously improved upon many of its initial limitations, yet novel applications of exponential smoothing have brought new forecasting challenges that have revealed additional pitfalls in its use. In this work, we examine potential reasons for these issues and argue that special attention should be drawn to the cost function used to estimate model parameters. Conventional cost functions assume that the postulated model is an accurate reflection of underlying demand, which is not the case for the majority of real applications. We propose the use of alternative cost functions based on multi-step ahead predictions and trace forecasts. We show that these are univariate shrinkage estimators. We describe the nature of shrinkage and show that it differs from established shrinkage approaches, such as ridge and LASSO regression, offering new modelling capabilities. Using retailing sales, we construct forecasts and empirically demonstrate this shrinkage, validate our theoretical understanding, and provide evidence of both economic and forecast accuracy gains. We discuss implications for practice and limitations of the shrinkage caused by the multi-step cost functions.
Related Topics
Concepts
Exponential smoothing
Univariate
Computer science
Estimator
Econometrics
Smoothing
Shrinkage
TRACE (psycholinguistics)
Lasso (programming language)
Mathematical optimization
Multivariate statistics
Machine learning
Economics
Statistics
Mathematics
World Wide Web
Computer vision
Philosophy
Linguistics
Metadata
- Type
- article
- Language
- en
- https://eprints.lancs.ac.uk/id/eprint/136732/1/KourentzesTrapero_2018_ETSshirnk.pdf
- OA Status
- green
- Cited By
- 1
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2974158300
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2974158300Canonical identifier for this work in OpenAlex
- Title
-
On the use of multi-step cost functions for generating forecastsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-01-01Full publication date if available
- Authors
-
Nikolaos Kourentzes, Juan R. TraperoList of authors in order
- PDF URL
-
https://eprints.lancs.ac.uk/id/eprint/136732/1/KourentzesTrapero_2018_ETSshirnk.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://eprints.lancs.ac.uk/id/eprint/136732/1/KourentzesTrapero_2018_ETSshirnk.pdfDirect OA link when available
- Concepts
-
Exponential smoothing, Univariate, Computer science, Estimator, Econometrics, Smoothing, Shrinkage, TRACE (psycholinguistics), Lasso (programming language), Mathematical optimization, Multivariate statistics, Machine learning, Economics, Statistics, Mathematics, World Wide Web, Computer vision, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2019: 1Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.empirically | 165 |
| abstract_inverted_index.established | 145 |
| abstract_inverted_index.estimators. | 132 |
| abstract_inverted_index.exponential | 41 |
| abstract_inverted_index.forecasting | 46 |
| abstract_inverted_index.limitations | 189 |
| abstract_inverted_index.operations. | 7 |
| abstract_inverted_index.parameters. | 82 |
| abstract_inverted_index.predictions | 121 |
| abstract_inverted_index.regression, | 153 |
| abstract_inverted_index.simplicity, | 16 |
| abstract_inverted_index.theoretical | 171 |
| abstract_inverted_index.Conventional | 83 |
| abstract_inverted_index.applications | 39 |
| abstract_inverted_index.continuously | 29 |
| abstract_inverted_index.implications | 185 |
| abstract_inverted_index.limitations, | 36 |
| abstract_inverted_index.applications. | 108 |
| abstract_inverted_index.capabilities. | 157 |
| abstract_inverted_index.implementation | 23 |
| abstract_inverted_index.understanding, | 172 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.64230817 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |