Real-time Intraday Traffic Volume Forecasting – A Hybrid Application Using Singular Spectrum Analysis and Artificial Neural Networks

The present paper provides a comparative evaluation of hybrid Singular Spectrum Analysis (SSA) and Artificial Neural Networks (ANN) against conventional ANN, applied on real time intraday traffic volume forecasting. The main research objective was to assess the applicability and functionality of intraday traffic volume forecasting, based on toll station measurements. The proposed methodology was implemented and evaluated upon a custom developed forecasting software toolbox, based on the software Mathworks MatLab, by using real data from Iasmos-Greece toll station. Experimental results demonstrated a superior ex post forecasting accuracy of the proposed hybrid forecasting methodology against conventional ANN, when compared to performance of usual statistical criteria (Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, Coefficient of Determination R2, Theil's inequality coefficient). The obtained results revealed that the hybrid model could advance forecasting accuracy of a conventional ANN model in intraday traffic volume forecasting, while embedding hybrid forecasting algorithm in an Intelligent Transport System could provide an advanced decision support module for transportation system maintenance, operation and management

Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis

This study examines the potential advantages of using Singular Spectrum Analysis (SSA) for forecasting tourism demand. To do this it examines the performance of SSA forecasts using monthly data for tourist arrivals into the United States over the period 1996 to 2012. The SSA forecasts are compared to those from a range of other forecasting approaches previously used to forecast tourism demand. These include ARIMA, exponential smoothing and neural networks. The results presented show that the SSA approach produces forecasts which perform (statistically) significantly better than the alternative methods in forecasting total tourist arrivals into the U.S. Forecasts using the SSA approach are also shown to offer a significantly better forecasting performance for arrivals into the U.S. from individual source countries. Of the alternative forecasting approaches exponential smoothing and feed-forward neural networks in particular were found to perform poorly. The key conclusion is that Singular Spectrum Analysis (SSA) offers significant advantages in forecasting tourist arrivals into the US and is worthy of consideration for other forecasting studies of tourism demand

Forecasting bus passenger flows by using a clustering-based support vector regression approach

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

On the use of Singular Spectrum Analysis for Forecasting U.S. Trade before, during and after the 2008 Recession

This paper is aimed at introducing the powerful, nonparametric time series analysis and forecasting technique of Singular Spectrum Analysis (SSA) for trade forecasting via an application which evaluates the impact of the 2008 recession on U.S. trade forecasting models. This research is felicitous given the magnitude of the structural break visible in the U.S. trade series following the 2008 economic crisis. Structural breaks resulting from such recessions might affect conclusions from traditional unit root tests and forecasting models which makes use of these tests. As such, it is prudent to evaluate the sensitivity and reliability of parametric, historical trade forecasting models in comparison to the relatively modern, nonparametric models. In doing so, we introduce the SSA technique for trade forecasting and perform exhaustive statistical tests on the data for normality, stationarity and change points, and the forecasting results for statistical significance prior to reaching the well-founded conclusion that SSA is less sensitive to the impact of recessions on U.S. trade, in comparison to an optimized ARIMA model, Exponential Smoothing and Neural Network models. Ergo, we conclude that SSA is able to provide more accurate forecasts for U.S. trade in the face of recessions, and is therefore presented as an apt alternative for U.S. trade forecasting before, during and after a future recession

Forecasting with Big Data: A Review

Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. In this paper we present a comprehensive review on the use of Big Data for forecasting by identifying and reviewing the problems, potential, challenges and most importantly the related applications. Skills, hardware and software, algorithm architecture, statistical significance, the signal to noise ratio and the nature of Big Data itself are identified as the major challenges which are hindering the process of obtaining meaningful forecasts from Big Data. The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks are the most common tools adopted for forecasting with Big Data

Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques

Author name used in this publication: K. W. Chau2010-2011 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe