8,492 research outputs found
Review of Nature-Inspired Forecast Combination Techniques
Effective and efficient planning in various areas can be significantly supported by forecasting a variable
like an economy growth rate or product demand numbers for a future point in time. More than one forecast for the same
variable is often available, leading to the question whether one should choose one of the single models or combine
several of them to obtain a forecast with improved accuracy. In the almost 40 years of research in the area of forecast
combination, an impressive amount of work has been done. This paper reviews forecast combination techniques that are
nonlinear and have in some way been inspired by nature
Dynamic Pooling for the Combination of Forecasts Generated Using Multi Level Learning
In this paper we provide experimental results and
extensions to our previous theoretical findings concerning the
combination of forecasts that have been diversified by three
different methods: with parameters learned at different data
aggregation levels, by thick modeling and by the use of different
forecasting methods. An approach of error variance based
pooling as proposed by Aiolfi and Timmermann has been compared
with flat combinations as well as an alternative pooling
approach in which we consider information about the used
diversification. An advantage of our approach is that it leads to
the generation of novel multi step multi level forecast generation
structures that carry out the combination in different steps of
pooling corresponding to the different types of diversification.
We describe different evolutionary approaches in order to
evolve the order of pooling of the diversification dimensions.
Extensions of such evolutions allow the generation of more
flexible multi level multi step combination structures containing
better adaptive capabilities. We could prove a significant error
reduction comparing results of our generated combination
structures with results generated with the algorithm of Aiolfi
and Timmermann as well as with flat combination for the
application of Revenue Management seasonal forecasting
Evolving Multilevel Forecast Combination Models - An Experimental Study
This paper provides a description and experimental comparison of different forecast combination techniques for
the application of Revenue Management forecasting for Airlines. In order to benefit from the advantages of forecasts predicting
seasonal demand using different forecast models on different aggregation levels and to reduce the risks of high noise terms on
low level predictions and overgeneralization on higher levels, various approaches based on combination of many predictions
are presented and experimentally compared. We propose to evolve combination structures dynamically using Evolutionary
Computing approaches. The evolved structures are not only able to generate predictions representing well balanced and stable
fusions of methods and levels, they are also characterised by high adaptive capabilities. The focus on different levels or methods
of forecasting may change as well as the complexity of the combination structure depending on changes in parts of the input
data space in different data aggregation levels. Significant forecast improvements have been obtained when using the proposed
dynamic multilevel structures
Forecasting and Forecast Combination in Airline Revenue Management Applications
Predicting a variable for a future point in time helps planning for unknown
future situations and is common practice in many areas such as economics, finance,
manufacturing, weather and natural sciences. This paper investigates and compares
approaches to forecasting and forecast combination that can be applied to service
industry in general and to airline industry in particular. Furthermore, possibilities to
include additionally available data like passenger-based information are discussed
The Dynamics of Real-Time Online Information and Disease Progression: Understanding Spatial Heterogeneity in the Relationship
The re-emergence of infectious diseases such as measles and polio is creating logistics challenges for the state authorities to curb their spread and contain them. (CL, 2015) Real-time surveillance of infectious diseases is important to detect possible epidemics in advance to prevent shortages of medications (FDA, 2018). The outbreak of an infectious disease creates panic in the community and is accompanied by a sudden increase in the online interest in knowing more about the disease and its symptoms. Prior studies have found a strong relationship between web-based information and disease outbreak but the influence of dynamics of web-based information in real-time is often not considered (Zhang, 2017). The dynamics or rate of change of the online interest in a disease can inform or misinform about perspective cases of the disease in a region. Oftentimes, especially in this connected world individuals overreact to the situation which may send spurious online signals regarding the disease progression. Hence, we study the relationship between the dynamics of online information and the infectious disease outbreak. We also investigate if this relationship could be influenced by regional demographic factors. We analyze weekly online interest dynamics for five infectious diseases over a period of three years across 50 states of the United States. We control for several factors (including weather, demographics, and travelers) and utilize hierarchical functional data models to incorporate real-time dynamics and clustering at the regional level. Preliminary findings suggest that online interest dynamics have a significant relationship with disease outbreak and the effect is segregated at the regional level. These findings are important to develop a system for real-time surveillance and account for the influence of heterogonous online interest during an endemic outbreak
Global supply chains of high value low volume products
Imperial Users onl
The impact of macroeconomic leading indicators on inventory management
Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study
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