Sensitivity analysis for time lag selection to forecast seasonal time series using neural networks and support vector machines

Multi-step ahead forecasting is an important issue for organizations, often used to assist in tactical decisions. Such forecasting can be achieved by adopting time series forecasting methods, such as the classical Holt-Winters (HW) that is quite popular for seasonal series. An alternative forecasting approach comes from the use of more flexible learning algorithms, such as Neural Networks (NN) and Support Vector Machines (SVM). This paper presents a simultaneous variable (i.e. time lag) and model selection algorithm for multi-step ahead forecasting using NN and SVM. Variable selection is based on a backward algorithm that is guided by a sensitivity analysis procedure, while model selection is achieved using a grid-search. Several experiments were devised by considering eight seasonal series and the forecasts were analyzed using two error criteria (i.e. SMAPE and MSE). Overall, competitive results were achieved when comparing the SVM and NN algorithms with HW.Fundação para a Ciência e Tecnologia (FCT) - Project PTDC/EIA/64541/2006

PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting

Seasonality is a distinctive characteristic which is often observed in many practical time series. Artificial Neural Networks (ANNs) are a class of promising models for efficiently recognizing and forecasting seasonal patterns. In this paper, the Particle Swarm Optimization (PSO) approach is used to enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman ANN (EANN) models for seasonal data. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here. The empirical analysis is conducted on three real-world seasonal time series. Results clearly show that each version of the PSO algorithm achieves notably better forecasting accuracies than the standard Backpropagation (BP) training method for both FANN and EANN models. The neural network forecasting results are also compared with those from the three traditional statistical models, viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters (HW) and Support Vector Machine (SVM). The comparison demonstrates that both PSO and BP based neural networks outperform SARIMA, HW and SVM models for all three time series datasets. The forecasting performances of ANNs are further improved through combining the outputs from the three PSO based models.Comment: 4 figures, 4 tables, 31 references, conference proceeding

Forecasting seasonal time series with computational intelligence: contribution of a combination of distinct methods

Accurate time series forecasting are important for displaying the manner in which the past contin- ues to affect the future and for planning our day to day activities. In recent years, a large litera- ture has evolved on the use of computational in- telligence in many forecasting applications. In this paper, several computational intelligence techniques (genetic algorithms, neural networks, support vec- tor machine, fuzzy rules) are combined in a distinct way to forecast a set of referenced time series. Fore- casting performance is compared to the a standard and method frequently used in practice.Project DAR 1M0572 of the MŠMT ČR

Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations

Accurate time series forecasting is a key issue to support individual and or- ganizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neu- ral networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on sea- sonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series.The research was supported by the European Regional Development Fund in the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070). Furthermore, we gratefully acknowledge partial support of the project KON- TAKT II - LH12229 of MSˇMT CˇR

Modelling atmospheric ozone concentration using machine learning algorithms

Air quality monitoring is one of several important tasks carried out in the area of environmental science and engineering. Accordingly, the development of air quality predictive models can be very useful as such models can provide early warnings of pollution levels increasing to unsatisfactory levels. The literature review conducted within the research context of this thesis revealed that only a limited number of widely used machine learning algorithms have been employed for the modelling of the concentrations of atmospheric gases such as ozone, nitrogen oxides etc. Despite this observation the research and technology area of machine learning has recently advanced significantly with the introduction of ensemble learning techniques, convolutional and deep neural networks etc. Given these observations the research presented in this thesis aims to investigate the effective use of ensemble learning algorithms with optimised algorithmic settings and the appropriate choice of base layer algorithms to create effective and efficient models for the prediction and forecasting of specifically, ground level ozone (O3). Three main research contributions have been made by this thesis in the application area of modelling O3 concentrations. As the first contribution, the performance of several ensemble learning (Homogeneous and Heterogonous) algorithms were investigated and compared with all popular and widely used single base learning algorithms. The results have showed impressive prediction performance improvement obtainable by using meta learning (Bagging, Stacking, and Voting) algorithms. The performances of the three investigated meta learning algorithms were similar in nature giving an average 0.91 correlation coefficient, in prediction accuracy. Thus as a second contribution, the effective use of feature selection and parameter based optimisation was carried out in conjunction with the application of Multilayer Perceptron, Support Vector Machines, Random Forest and Bagging based learning techniques providing significant improvements in prediction accuracy. The third contribution of research presented in this thesis includes the univariate and multivariate forecasting of ozone concentrations based of optimised Ensemble Learning algorithms. The results reported supersedes the accuracy levels reported in forecasting Ozone concentration variations based on widely used, single base learning algorithms. In summary the research conducted within this thesis bridges an existing research gap in big data analytics related to environment pollution modelling, prediction and forecasting where present research is largely limited to using standard learning algorithms such as Artificial Neural Networks and Support Vector Machines often available within popular commercial software packages

Forecasting northern polar stratospheric variability using a hierarchy of statistical models

The northern polar stratosphere plays an important role in modulating the wintertime near-surface temperature conditions in midlatitudes. Forecasting northern polar stratospheric variability will have the potential to extend the winter weather forecasts in midlatitudes. As such, this research seeks to explore a novel approach of forecasting short-term northern polar stratospheric variability using a hierarchy of linear and non-linear statistical models. In addition to El Niño Southern Oscillation, the Quasi-biennial Oscillation, and the 11-year solar cycle indices, this research uses the upward flux of wave activity from the troposphere into the stratosphere as a predictor for modeling and forecasting northern polar stratospheric temperature and geopotential height anomalies. The upward flux of wave activity entering the stratosphere is the primary source of intraseasonal variability in the wintertime stratospheric polar vortex. Multiple linear regression and machine learning models were trained over the 1980-2005 time period, and the 10-day and 20-day northern polar stratospheric temperature and geopotential height forecasts were generated over the 2005-2011 time period. The importance of each predictor for modeling northern polar stratospheric variability was assessed using a permutation-based method. The study has found that the use of the meridional wave heat flux predictors improves the accuracy of short-term northern polar stratospheric geopotential height forecasts as demonstrated by the correlation coefficient of 0.48 over the 2005-2011 time period. In contrast to previous studies, multiple linear regression shows better predictive performance than the machine learning models over the 2005-2011 time period. A better predictive performance of multiple linear regression in comparison to machine learning models is due to a much higher contribution of the upward flux of wave activity than other predictors to forecast skill

Forecasting store foot traffic using facial recognition, time series and support vector machines

In this paper, we explore data collected in a pilot project that used a digital camera and facial recognition to detect foot traffic to a sports store. Using a time series approach, we model daily incoming store traffic under three classes (all faces, female, male) and compare six forecasting approaches, including Holt-Winters (HW), a Support Vector Machine (SVM) and a HW-SVM hybrid that includes other data features (e.g., weather conditions). Several experiments were held, under a robust rolling windows scheme that considers up to one week ahead predictions and two metrics (predictive error and estimated store benefit). Overall, competitive results were achieved by the SVM (all faces), HW (female) and HW-SVM (male) methods, which can potentially lead to valuable gains (e.g., enhancing store marketing or human resource management).This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013