Predicting price trends of digital products using various forecasting techniques : on the example of the steam community market

The gaming industry has experienced steady growth over the years, contributing to its increasing commercialisation. One factor adding to this trend is the growing popularity of online marketplaces for in­game items, some of which are traded using real­world currencies and considered by a growing amount of young people as some new asset class. This study addresses the question of price predictability for these items, as the efficient market hypothesis posits that it is impossible to consistently predict future prices based on past prices. While this topic has been extensively discussed in the literature for classical financial time series forecasting, it has not yet been explored in the context of in­game item marketplaces. This study used data from the Steam Community Market to investigate the predictability of ingame item prices in the context of online marketplaces. Multiple linear and non­linear forecasting models are applied to the data. This study shows that the price is predictable to some degree for many items, although the improvement is small compared to the naïve benchmark. Specially, linear models showed auspicious results for stationary data and short­term predictions, while non­linear models rarely delivered a strong performance. These findings suggest that forecasting digital items may be as challenging as forecasting traditional assets.A indústria do jogo tem verificado um crescimento constante ao longo dos anos, contribuindo para a sua crescente comercialização. Um fator que contribui para esta tendência é a crescente popularidade dos mercados online para artigos dentro do jogo, alguns dos quais são comercializados utilizando moedas do mundo real e considerados por uma quantidade crescente de jovens como uma espécie de nova classe de ativos. Este estudo aborda a questão da previsibilidade de preços para estes itens, uma vez que a hipótese de mercado eficiente postula que é impossível prever de forma consistente os preços futuros com base nos preços do passado. Embora este tópico tenha sido amplamente discutido na literatura para a previsão clássica de séries cronológicas financeiras, ainda não foi explorado no contexto dos mercados de itens dentro do jogo. Este estudo utilizou dados Steam Community Market para investigar a previsibilidade dos preços dos itens no contexto dos mercados online. Modelos múltiplos de previsão linear e não­linear são aplicados aos dados. Este estudo mostra que o preço é previsível até certo ponto para muitos itens, embora a melhoria seja pequena em comparação com a referência naïve. Especialmente os modelos lineares mostraram resultados auspiciosos para dados estacionários e previsões a curto prazo, enquanto os modelos não lineares raramente proporcionaram um forte desempenho. Estes resultados sugerem que a previsão de itens digitais pode ser tão desafiante como a previsão de bens tradicionais

Modelling cross-dependencies between Spain's regional tourism markets with an extension of the Gaussian process regression model

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level

Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model

This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism demand to all seventeen regions of Spain, the performance of the proposed model is assessed in a multiple-step-ahead forecasting comparison. The results of the experiment in a multivariate setting show that the Gaussian process regression model significantly improves the forecasting accuracy of a multi-layer perceptron neural network used as a benchmark. The results reveal that incorporating the connections between different markets in the modelling process may prove very useful to refine predictions at a regional level.Peer ReviewedPostprint (author's final draft

Meta-learning for Forecasting Model Selection

Model selection for time series forecasting is a challenging task for practitioners and academia. There are multiple approaches to address this, ranging from time series analysis using a series of statistical tests, to information criteria or empirical approaches that rely on cross-validated errors. In recent forecasting competitions, meta-learning obtained promising results establishing its place as a model selection alternative. Meta-learning constructs meta-features for each time series and trains a classifier on these to choose the most appropriate forecasting method. In the first part, this thesis studies the main components of meta-learning and analyses the effect of alternative meta-features, meta-learners, and base forecasters in the final model selection results. We investigate different meta-learners, the use of simple or complex base forecasts, and a large and diverse set of meta-features. Our findings show that stationarity tests, which identify the presence of unit root in time series, and proxies of autoregressive information, which show the strength of serial correlation in a series, have the highest importance for the performance of meta-learning. On the contrary, features related to time series quantiles and other descriptive statistics such as the mean, and the variance exhibit the lowest importance. Furthermore, we observe that using simple base forecasters is more sensitive to the number of groups of features employed as meta-feature and overall had worse performed. In terms of the choice of learners, classifiers with evidence of good performance in the literature resulted in the most accurate meta-learners. The success of meta-learning largely depends on its building components. The selection and generation of the appropriate meta-features remains a major challenge in meta-learning. In the second part, we propose using Convolutional Neural Networks (CNN) to overcome this. CNN have demonstrated breakthrough accuracy in pattern recognition tasks and can generate features as needed internally, within its layers, without intervention from the modeller. Using CNN, we provide empirical evidence of the efficacy of the approach, against widely accepted forecast selection methods and discuss the advantages and limitations of the proposed approach. Finally, we provide additional evidence that using meta-learning, for automated model selection, outperformed all of the individual benchmark forecasts

Machine learning for inventory management: forecasting demand quantiles of perishable products with a neural network

Accurate demand forecasting is a crucial component in building an efficient supply chain. Forecasting is a major determinant of inventory cost. Several methods and models for forecasting have been studied extensively over the last decades. In recent years, there has been a growing interest in the capabilities of Machine Learning algorithms in forecasting, and specifically in Neural Network models. Despite the expanding research on forecasting with Neural Networks, there have been only few studies focusing on the specific ramifications for forecasting demand of perishable products at the Stock Keeping Unit (SKU) level. Forecasting SKU-level demand for perishable products is a challenging task: time series for demand are volatile, skewed, subject to external factors, and frequently consist of only a few observations. Furthermore, SKU-level demand forecasts are typically used for inventory management, which imposes additional requirements on the forecasting procedure. This study examines how to design Neural Networks that address the specific ramifications of inventory management for several thousand SKUs. This work identifies central issues in the field and compiles successful approaches to overcome them. Next, a Neural Network architecture is suggested that takes these special requirements into account, building on insights from the literature. Namely, it learns from multiple hundred time series, incorporates external data into the prediction, and provides quantile forecasts of cumulative demand. In a large-scale experiment, the model forecasted the demand for several hundred SKUs in the fresh product segment of a German wholesale company. These forecasts were subsequently used for simulating the inventory development at the company for three months under close-to-real-life conditions. This study shows that Neural Networks are a promising approach to deal with large-scale forecasting problems for perishable products. The main finding of this study is that within the experimental setting, the base form of the suggested model for accurate daily demand forecasting yielded superior results to an array of competing baselines. In terms of inventory performance, the results are mixed, but present exciting directions for further research