Modelling Deterministic Seasonality with Artificial Neural Networks for Time Series Forecasting

This study explores both from a theoretical and empirical perspective how to model deterministic seasonality with artificial neural networks (ANN) to achieve the best forecasting accuracy. The aim of this study is to maximise the available seasonal information to the ANN while identifying the most economic form to code it; hence reducing the modelling degrees of freedom and simplifying the network’s training. An empirical evaluation on simulated and real data is performed and in agreement with the theoretical analysis no deseasonalising is required. A parsimonious coding based on seasonal indices is proposed that showed the best forecasting accuracy

Distributions of forecasting errors of forecast combinations: implications for inventory management

Inventory control systems rely on accurate and robust forecasts of future demand to support decisions such as setting of safety stocks. Combining forecasts is shown to be effective not only in reducing forecast errors, but also in being less sensitive to limitations of a single model. Research on forecast combination has primarily focused on improving accuracy, largely ignoring the overall shape and distribution of forecast errors. Nonetheless, these are essential for managing the level of aversion to risk and uncertainty for companies. This study examines the forecast error distributions of base and combination forecasts and their implications for inventory performance. It explores whether forecast combinations transform the forecast error distribution towards desired properties for safety stock calculations, typically based on the assumption of normally distributed errors and unbiased forecasts. In addition, it considers the similarity between in- and out-of-sample characteristics of such errors and the impact of different lead times. The effects of established combination methods are explored empirically using a representative set of forecasting methods and a dataset of 229 weekly demand series from a household and personal care leading UK manufacturer. Findings suggest that forecast combinations make the in- and out-of-sample behaviour more consistent, requiring less safety stock on average than base forecasts. Furthermore we find that using in-sample empirical error distributions of combined forecasts approximates well the out-of-sample ones, in contrast to base forecasts

Advances in forecasting with artificial neural networks

There is decades long research interest in artificial neural networks (ANNs) that has led to several successful applications. In forecasting, both in theoretical and empirical works, ANNs have shown evidence of good performance, in many cases outperforming established benchmark models. However, our understanding of their inner workings is still limited, which makes it difficult for academicians and practitioners alike to use them. Furthermore, while there is a growing literature supporting their good performance in forecasting, there is also a lot of scepticism whether ANNs are able to provide reliable and robust forecasts. This analysis presents the advances of ANNs in the time series forecasting field, highlighting the current state of the art, which modelling issues have been solved and which are still critical for forecasting with ANNs, indicating future research directions

An early warning method for agricultural products price spike based on artificial neural networks prediction

In general, the agricultural producing sector is affected by the diversity in supply, mostly from small companies, in addition to the rigidity of the demand, the territorial dispersion, the seasonality or the generation of employment related to the rural environment. These characteristics differentiate the agricultural sector from other economic sectors. On the other hand, the volatility of prices payed by producers, the high cost of raw materials, and the instability of both domestic and international markets are factors which have eroded the competitiveness and profitability of the agricultural sector. Because of the advance in technology, applications have been developed based on Artificial Neural Networks (ANN) which have helped the development of sales forecast on consumer products, improving the accuracy of traditional forecasting systems. This research uses the RNA to develop an early warning system for facing the increase in agricultural products, considering macro and micro economic variables and factors related to the seasons of the year

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Validation in models of climate change and forecasting accuracy

Forecasting researchers, with few exceptions, have ignored the major forecasting controversy facing the world in the early 21st Century: namely, whether and by how much the earth is warming; and the role of climate modelling in reaching any conclusions on this challenging topic. In contrast, scientists from climatologists, through hydrologists to fluid dynamicists, have engaged in this modelling and forecasting controversy. In this discussion paper, we first describe briefly the atmospheric-ocean general circulation models (AOGCM) used in most climate forecasting, in particular by the Intergovernmental Panel on Climate Change (IPCC). This discussion paper takes a forecaster’s perspective in a review of established principles for the validation of such large-scale simulation models. One key principle is that such models should reproduce the ‘stylised facts’ or 'dominant modes' of dynamic behaviour that characterize key model outputs: here taken as the aggregate annual changes in world and regional temperatures. By developing various time series models and input-output dynamic models of atmospheric carbon dioxide and temperature that capture current trends, we compare the results with dynamic forecasts produced by one well-established AOGCM model, the Hadley Centre’s HadCM3. Time series models are shown to perform strongly and by using encompassing tests, structural deficiencies are identified in the AOGCM model and its corresponding forecasts. The paper concludes with some implications for climate modellers when producing decade-ahead forecasts from global climate models. If forecasting accuracy is the focus, methods that combine standard time series methods with the structure of a GCM should be used. This has implications for the effectiveness of control policies, focussed on carbon dioxide emissions alone.

Impact of Information Exchange on Supplier Forecasting Performance

Forecasts of demand are crucial to drive supply chains and enterprise resource planning systems. Usually, well-known univariate methods that work automatically such as exponential smoothing are employed to accomplish such forecasts. The traditional Supply Chain relies on a decentralised system where each member feeds its own Forecasting Support System (FSS) with incoming orders from direct customers. Nevertheless, other collaboration schemes are also possible, for instance, the Information Exchange framework allows demand information to be shared between the supplier and the retailer. Current theoretical models have shown the limited circumstances where retailer information is valuable to the supplier. However, there has been very little empirical work carried out. This works assesses the role of sharing market sales information obtained by the retailer on the supplier forecasting accuracy. Data have been collected from a manufacturer of domestic cleaning products and a major UK grocery retailer to show the circumstances where information sharing leads to improved accuracy. We find significant evidence of benefits through information sharing