| 1. |
- Fildes, Robert, et al.
(författare)
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Validation and forecasting accuracy in models of climate change
- 2011
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 27:4, s. 968-995
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Tidskriftsartikel (refereegranskat)abstract
- Forecasting researchers, with few exceptions, have ignored the current major forecasting controversy: global warming and the role of climate modelling in resolving this challenging topic. In this paper, we take a forecaster's perspective in reviewing established principles for validating the atmospheric-ocean general circulation models (AOGCMs) used in most climate forecasting, and in particular by the Intergovernmental Panel on Climate Change (IPCC). Such models should reproduce the behaviours characterising key model outputs, such as global and regional temperature changes. We develop various time series models and compare them with forecasts based on one well-established AOGCM from the UK Hadley Centre. Time series models perform strongly, and structural deficiencies in the AOGCM forecasts are identified using encompassing tests. Regional forecasts from various GCMs had even more deficiencies. We conclude that combining standard time series methods with the structure of AOGCMs may result in a higher forecasting accuracy. The methodology described here has implications for improving AOGCMs and for the effectiveness of environmental control policies which are focussed on carbon dioxide emissions alone. Critically, the forecast accuracy in decadal prediction has important consequences for environmental planning, so its improvement through this multiple modelling approach should be a priority.
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| 2. |
- Ramos, Patrícia, et al.
(författare)
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Forecasting Seasonal Sales with Many Drivers : Shrinkage or Dimensionality Reduction?
- 2023
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Ingår i: Applied System Innovation. - : MDPI. - 2571-5577. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Retailers depend on accurate forecasts of product sales at the Store × SKU level to efficiently manage their inventory. Consequently, there has been increasing interest in identifying more advanced statistical techniques that lead to accuracy improvements. However, the inclusion of multiple drivers affecting demand into commonly used ARIMA and ETS models is not straightforward, particularly when many explanatory variables are available. Moreover, regularization regression models that shrink the model’s parameters allow for the inclusion of a lot of relevant information but do not intrinsically handle the dynamics of the demand. These problems have not been addressed by previous studies. Nevertheless, multiple simultaneous effects interacting are common in retailing. To be successful, any approach needs to be automatic, robust and efficiently scaleable. In this study, we design novel approaches to forecast retailer product sales taking into account the main drivers which affect SKU demand at store level. To address the variable selection challenge, the use of dimensionality reduction via principal components analysis (PCA) and shrinkage estimators was investigated. The empirical results, using a case study of supermarket sales in Portugal, show that both PCA and shrinkage are useful and result in gains in forecast accuracy in the order of 10% over benchmarks while offering insights on the impact of promotions. Focusing on the promotional periods, PCA-based models perform strongly, while shrinkage estimators over-shrink. For the non-promotional periods, shrinkage estimators significantly outperform the alternatives.
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| 3. |
- Schaer, Oliver, et al.
(författare)
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Demand forecasting with user-generated online information
- 2019
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 35:1, s. 197-212
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Tidskriftsartikel (refereegranskat)abstract
- Recently, there has been substantial research on the augmentation of aggregate forecasts with individual consumer data from internet platforms, such as search traffic or social network shares. Although the majority of studies have reported increases in accuracy, many exhibit design weaknesses, including a lack of adequate benchmarks or rigorous evaluation. Furthermore, their usefulness over the product life-cycle has not been investigated, even though this may change, as consumers may search initially for pre-purchase information, but later for after-sales support. This study begins by reviewing the relevant literature, then attempts to support the key findings using two forecasting case studies. Our findings are in stark contrast to those in the previous literature, as we find that established univariate forecasting benchmarks, such as exponential smoothing, consistently perform better those that include online information. Our research underlines the need for a thorough forecast evaluation and argues that the usefulness of online platform data for supporting operational decisions may be limited.
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| 4. |
- Schaer, Oliver, et al.
(författare)
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Predictive competitive intelligence with prerelease online search traffic
- 2022
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Ingår i: Production and operations management. - : John Wiley & Sons. - 1059-1478 .- 1937-5956. ; 31:10, s. 3823-3839
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Tidskriftsartikel (refereegranskat)abstract
- In today's competitive market environment, it is vital for companies to gain insight about competitors' new product launches. Past studies have demonstrated the predictive value of prerelease online search traffic (PROST) for new product forecasting. Relying on these findings and the public availability of PROST, we investigate its usefulness for estimating sales of competing products. We propose a model for predicting the success of competitors' product launches, based on own past product sales data and competitor's prerelease Google Trends. We find that PROST increases predictive accuracy by more than 18% compared to models that only use internally available sales data and product characteristics of video game sales. We conclude that this inexpensive source of competitive intelligence can be helpful when managing the marketing mix and planning new product releases.
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| 5. |
- Sroginis, Anna, et al.
(författare)
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Use of contextual and model-based information in adjusting promotional forecasts
- 2023
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Ingår i: European Journal of Operational Research. - : Elsevier. - 0377-2217 .- 1872-6860. ; 307:3, s. 1177-1191
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Tidskriftsartikel (refereegranskat)abstract
- Despite improvements in statistical forecasting, human judgment remains fundamental to business forecasting and demand planning. Typically, forecasters do not rely solely on statistical forecasts; they also adjust forecasts according to their knowledge, experience, and information that is not available to statistical models. However, we have limited understanding of the adjustment mechanisms employed, particularly how people use additional information (e.g., special events and promotions, weather, holidays) and under which conditions this is beneficial. Using a multi-method approach, we first analyse a UK retailer case study exploring its operations and the forecasting process. The case study provides a contextual setting for the laboratory experiments that simulate a typical supply chain forecasting process. In the experimental study, we provide past sales, statistical forecasts (using baseline and promotional models) and qualitative information about past and future promotional periods. We include contextual information, with and without predictive value, that allows us to investigate whether forecasters can filter such information correctly. We find that when adjusting, forecasters tend to focus on model-based anchors, such as the last promotional uplift and the current statistical forecast, ignoring past baseline promotional values and additional information about previous promotions. The impact of contextual statements for the forecasting period depends on the type of statistical predictions provided: when a promotional forecasting model is presented, people tend to misinterpret the provided information and over-adjust, harming accuracy.
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| 6. |
- Trapero, Juan R., et al.
(författare)
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On the identification of sales forecasting models in the presence of promotions
- 2015
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Ingår i: Journal of the Operational Research Society. - : Taylor & Francis. - 0160-5682 .- 1476-9360. ; 66:2, s. 299-307
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Tidskriftsartikel (refereegranskat)abstract
- Shorter product life cycles and aggressive marketing, among other factors, have increased the complexity of sales forecasting. Forecasts are often produced using a Forecasting Support System that integrates univariate statistical forecasting with managerial judgment. Forecasting sales under promotional activity is one of the main reasons to use expert judgment. Alternatively, one can replace expert adjustments by regression models whose exogenous inputs are promotion features (price, display, etc). However, these regression models may have large dimensionality as well as multicollinearity issues. We propose a novel promotional model that overcomes these limitations. It combines Principal Component Analysis to reduce the dimensionality of the problem and automatically identifies the demand dynamics. For items with limited history, the proposed model is capable of providing promotional forecasts by selectively pooling information across established products. The performance of the model is compared against forecasts provided by experts and statistical benchmarks, on weekly data; outperforming both substantially.
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