The Impact of Special Days in Call Arrivals Forecasting:A Neural Network Approach to Modelling Special Days

A key challenge for call centres remains the forecasting of high frequency call arrivals collected in hourly or shorter time buckets. In addition to the complex intraday, intraweek and intrayear seasonal cycles, call arrival data typically contain a large number of anomalous days, driven by the occurrence of holidays, special events, promotional activities and system failures. This study evaluates the use of a variety of univariate time series forecasting methods for forecasting intraday call arrivals in the presence of such outliers. Apart from established, statistical methods, we consider artificial neural networks (ANNs). Based on the modelling flexibility of the latter, we introduce and evaluate different methods to encode the outlying periods. Using intraday arrival series from a call centre operated by one of Europe’s leading entertainment companies, we provide new insights on the impact of outliers on the performance of established forecasting methods. Results show that ANNs forecast call centre data accurately, and are capable of modelling complex outliers using relatively simple outlier modelling approaches. We argue that the relative complexity of ANNs over standard statistical models is offset by the simplicity of coding multiple and unknown effects during outlying periods.NOTICE: this is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, [264, 3, (2016)] DOI: 10.1016/j.ejor.2016.07.015© 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0

Model Selection of Ensemble Forecasting Using Weighted Similarity of TIME Series

Several methods have been proposed to combine the forecasting results into single forecast namely the simple averaging, weighted average on validation performance, or non-parametric combination schemas. These methods use fixed combination of individual forecast to get the final forecast result. In this paper, quite different approach is employed to select the forecasting methods, in which every point to forecast is calculated by using the best methods used by similar training dataset. Thus, the selected methods may differ at each point to forecast. The similarity measures used to compare the time series for testing and validation are Euclidean and Dynamic Time Warping (DTW), where each point to compare is weighted according to its recentness. The dataset used in the experiment is the time series data designated for NN3 Competition and time series generated from the frequency of USPTO’s patents and PubMed’s scientific publications on the field of health, namely on Apnea, Arrhythmia, and Sleep Stages. The experimental result shows that the weighted combination of methods selected based on the similarity between training and testing data may perform better compared to either the unweighted combination of methods selected based on the similarity measure or the fixed combination of best individual forecast. Beberapa metode telah diajukan untuk menggabungkan beberapa hasil forecasting dalam single forecast yang diberi nama simple averaging, pemberian rata-rata dengan bobot pada tahap validasi kinerja, atau skema kombinasi non-parametrik. Metode ini menggunakan kombinasi tetap pada individual forecast untuk mendapatkan hasil final dari forecast. Dalam paper ini, pendekatan berbeda digunakan untuk memilih metode forecasting, di mana setiap titik dihitung dengan menggunakan metode terbaik yang digunakan oleh dataset pelatihan sejenis. Dengan demikian, metode yang dipilih dapat berbeda di setiap titik perkiraan. Similarity measure yang digunakan untuk membandingkan deret waktu untuk pengujian dan validasi adalah Euclidean dan Dynamic Time Warping (DTW), di mana setiap titik yang dibandingkan diberi bobot sesuai dengan keterbaruannya. Dataset yang digunakan dalam percobaan ini adalah data time series yang didesain untuk NN3 Competition dan data time series yang di-generate dari paten-paten USPTO dan publikasi ilmiah PubMed di bidang kesehatan, yaitu pada Apnea, Aritmia, dan Sleep Stages. Hasil percobaan menunjukkan bahwa pemberian kombinasi bobot dari metode yang dipilih berdasarkan kesamaan antara data pelatihan dan data pengujian, dapat menyajikan hasil yang lebih baik dibanding salah satu kombinasi metode unweighted yang dipilih berdasarkan similarity measure atau kombinasi tetap dari individual forecast terbaik

Multiple-input multiple-output vs. single-input single-output neural network forecasting

This study attempts to improve the forecasting accuracy of tourism demand by using the existing common trends in tourist arrivals form all visitor markets to a specific destination in a multiple-input multiple-output (MIMO) structure. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the MIMO approach does not outperform the forecasting accuracy of the networks when applied country by country, but it significantly improves the forecasting performance for total tourist arrivals. When comparing the forecast accuracy of the different models, we find that radial basis function networks outperform multilayer-perceptron and Elman networks

Multiple-input multiple-output vs. single-input single-output neural network forecasting

Working PapersThis study attempts to improve the forecasting accuracy of tourism demand by using the existing common trends in tourist arrivals form all visitor markets to a specific destination in a multiple-input multiple-output (MIMO) structure. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the MIMO approach does not outperform the forecasting accuracy of the networks when applied country by country, but it significantly improves the forecasting performance for total tourist arrivals. When comparing the forecast accuracy of the different models, we find that radial basis function networks outperform multilayer-perceptron and Elman networks.Preprin

Data-driven inventory optimization

The recent explosion of data availability opens up opportunities for companies to make better decisions. However, it is not clear, in general, how to get from data to a good decision. Exploiting these data for improved decision making requires adequate method- ologies. Inventory management decisions are a particularity important set of decision problems for virtually every company that buys, produces, distributes, or sells physical products. In this dissertation, we investigate the question of how to get from data to a good decision in inventory management problems. To this end, we revisit three fundamental inventory management problems, propose new data-driven methodologies, and measure their impact on inventory performance. Chapter II covers the newsvendor problem. To investigate how to exploit the available data, we propose a framework that distinguishes three levels on which data can generate value. Furthermore, we present a novel solution method that integrates the traditionally separate steps of demand estimation and inventory optimization into a single optimization problem. In our empirical analysis with real-world data, we find that data-driven methods outperform traditional approaches in most cases and that the benefit of improved forecasting dominates other potential benefits of data-driven methodologies. Chapter III is concerned with managing inventories for multiple products in a product category. We present a novel data-driven solution approach based on machine learning that integrates the estimation and opti- mization steps and takes complex substitution effects into account. We evaluate our approach on two real-world datasets. We find that our data-driven approach outperforms the benchmark on the first dataset and performs competitively on the second. Chapter IV focuses on dynamic inventory problems. We propose a novel solution approach that leverages auxiliary data. Our approach divides the problem into multiple single-stage problems using dynamic programming and uses machine learning methods in each stage to improve inventory decisions. In a computational study, we find that our method performs close to the optimal decision and significantly outperforms the benchmark

A Model for Stock Price Prediction Using the Soft Computing Approach

A number of research efforts had been devoted to forecasting stock price based on technical indicators which rely purely on historical stock price data. However, the performances of such technical indicators have not always satisfactory. The fact is, there are other influential factors that can affect the direction of stock market which form the basis of market experts’ opinion such as interest rate, inflation rate, foreign exchange rate, business sector, management caliber, investors’ confidence, government policy and political effects, among others. In this study, the effect of using hybrid market indicators such as technical and fundamental parameters as well as experts’ opinions for stock price prediction was examined. Values of variables representing these market hybrid indicators were fed into the artificial neural network (ANN) model for stock price prediction. The empirical results obtained with published stock data show that the proposed model is effective in improving the accuracy of stock price prediction. Also, the performance of the neural network predictive model developed in this study was compared with the conventional Box-Jenkins autoregressive integrated moving average (ARIMA) model which has been widely used for time series forecasting. Our findings revealed that ARIMA models cannot be effectively engaged profitably for stock price prediction. It was also observed that the pattern of ARIMA forecasting models were not satisfactory. The developed stock price predictive model with the ANN-based soft computing approach demonstrated superior performance over the ARIMA models; indeed, the actual and predicted value of the developed stock price predictive model were quite close

Topological optimisation of artificial neural networks for financial asset forecasting

The classical Artificial Neural Network (ANN) has a complete feed-forward topology, which is useful in some contexts but is not suited to applications where both the inputs and targets have very low signal-to-noise ratios, e.g. financial forecasting problems. This is because this topology implies a very large number of parameters (i.e. the model contains too many degrees of freedom) that leads to over fitting of both signals and noise. This results in the ANN having very good in-sample performance on the data used for its training but poor performance outof-sample for forecasting. The main contribution of my research is to develop a new heuristic method called “ANN reduction” for optimising the topological structure of a feed-forward ANN in order to improve its out-of-sample performance (using an RMS measure). The research concentrated on the topological optimization of the graph representing an ANN, which reduces the effective degrees of freedom of the ANN whilst still maintaining its feed-forward (but incomplete) topology. Such reductions in the number of parameters have been attempted before in the literature, but our procedure is of a different (graph theoretic) nature and (in extremis) optimal for small-size ANNs. Two applications of the ANN reduction are also implemented and programmed for empirical simulations. For this purpose, two datasets generated from deterministic functions and three datasets derived from foreign exchange market prices are used for evaluating the ANN reduction applications. These applications generate new ANN topologies with some clear performance advantages over those obtained by the best complete ANNs, improving the generalization (out-of-sample) performance by up to 27.6% compared to the complete ANN on the function generated datasets and up to 14.1% on the financial forecasting problem for the FX data

Corn and soybean basis behavior and forecasting: fundamental and alternative approaches

A model is developed to analyze the nearby basis behavior of corn and soybeans in several markets across the U.S. Results suggest that basis behavior has a seasonal pattern, and that different variables affect nearby corn and soybean basis at various periods. The most important factors affecting basis are storage cost (opportunity cost), transportation cost (barge rates), and supply (regional production relative to regional storage capacity). Nine conventional (naive three-year-average forecasts, econometric, ARIMA and composite forecast models) and less conventional forecasting techniques (State Space and Neural Networks models) are utilized to forecast out-of-sample basis for one-month up to 12-month ahead. The performance of all methods in forecasting 1991-1995 basis is analyzed. The forecasting performance comparison shows that adding current market information to the three-year-average model and the seasonal ARIMA model can slightly improve basis forecasts compared to the benchmark simple three-year-average forecast model

Forecasting: theory and practice

Forecasting has always been in 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 lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer 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, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. 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 the forecasting theory and practice