Comparing forecasting effectiveness through air travel data

Airline traffic forecasting in the medium term is important to airlines and regulatory authorities that attempt to plan and schedule capacity. This study examines a number of alternative approaches to forecasting short to medium term (1 to 3 years) air traffic flows. The data examined are flows between the UK and six other countries over the period of 1961- 2002, which has seen substantial changes in both transport technology and economic development. The economic drivers, under consideration, are price, income and bilateral trade. The forecasting models employed include autoregressive models, autoregressive distributed lag models specified using various statistical and economic criteria and a newly developed automatic method for model specification(PcGets), as well as time varying parameter models.Various approaches to including interactions between the contemporaneous air trriffic flows are examined including pooled autoregressive distributed lag models and the inclusion of a 'world' variable that measures overall trade growth in the world economy. Based on the analysis of forecasting error measures, it is concluded that time varying parameter models that include the 'world' variable with an average error of around 2.5% outperform alternative forecasting models. This is perhaps explained by the dramatic structural changes seen in the air traffic market

Algorithmic approaches in model selection of the air passengers flows data

Algorithm is an important element in any problem solving situation.In statistical modelling strategy, the algorithm provides a step by step process in model building, model testing, choosing the ‘best’ model and even forecasting using the chosen model.Tacit knowledge has contributed to the existence of a huge variability in manual modelling process especially between expert and non-expert modellers.Many algorithms (automated model selection) have been developed to bridge the gap either through single or multiple equation modelling.This study aims to evaluate the forecasting performances of several selected algorithms on air passengers flow data based on Root Mean Square Error (RMSE) and Geometric Root Mean Square Error (GRMSE).The findings show that multiple models selection performed well in one and two step-ahead forecast but was outperformed by single model in three step-ahead forecasts

Assessing the simulation performances of multiple model selection algorithm

The Autometrics is an algorithm for single equation model selection.It is a hybrid method which combines expanding and contracting search techniques.In this study, the algorithm is extended for multiple equations modelling known as SURE-Autometrics.The aim of this paper is to assess the performance of the extended algorithm using various simulation experiment conditions. The capability of the algorithm in finding the true specification of multiple models is measured by the percentage of simulation outcomes.Overall results show that the algorithm has performed well for a model with two equations.The findings also indicated that the number of variables in the true models affect the algorithm performances. Hence, this study suggests improvement on the algorithm development for future research

Blood cell image segmentation using unsupervised clustering techniques

In blood cell image analysis, segmentation is an indispensable step in quantitative cytophotometry. Blood cell images have become particularly useful in medical diagnostic tools for cases involving blood. The aim of our research is to develop an effective algorithm for segmentation of the blood cell images. In this paper, we present a framework of comparison cell images segmentation by using unsupervised clustering techniques with the purpose of acquiring the best method to segment the cell images. We use blood cell images infected with malaria parasites as cell images for our framework. Methods that involved in this comparison framework are Fuzzy C Means, K Means and Means Shift Analysis. The outcome from these methods will help to identify which technique or algorithm is the best for cell segmentation. Results from the segmented cell will be use for further classification and recognition

Comparative analysis on blood cell image segmentation

Image segmentation is an important phase in image recognition system.In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry. Currently, blood cell images become predominantly valuable in medical diagnostics tools.In this paper, we present a comparative analysis on several segmentation algorithms. Three selected common approaches, that are Fuzzy c-means, K-means and Mean-shift were presented.Blood cell images that are infected with malaria parasites at various stages were tested.The most suitable method that is K-means was selected. K-means has been enhanced by integrating Median-cut algorithm to further improve the segmentation process.The proposed integrated method has shown a significant improvement in the number of selected regions

Automated time series forecasting

Forecasting is one of the important tools in business environment because it assists in decision-making of strategic planning and controlling activities. Good planning and controlling procedure would lead to successful business.There are two categories of forecasting techniques; namely qualitative and quantitative.Qualitative technique is more towards judgmental forecasting and usually used when data is limited. While quantitative technique is based on statistical concepts and requires large amount of data in order to formulate the mathematical models.This technique can be classified into projective and causal technique.The projective technique (or univariate modelling) just involve one variable while the causal technique (or econometric modelling) suitable for multi-variables.Since forecasting involves uncertainty, several methods need to be executed on one set of time series data in order to produce accurate forecast.Hence, usually in practice forecaster need to use several softwares to obtain the forecast values.If this practice can be transformed into algorithm (well-defined rules for solving a problem) and then the algorithm can be transformed into a computer program, less time will be needed to compute the forecast values where in business world time is money.In this study, we focused on algorithm development for univariate forecasting techniques only and will expand towards econometric modelling in the future.Two set of simulated data (yearly and non-yearly) and several univariate forecasting techniques (i.e. Moving Average, Decomposition, Exponential Smoothing, Time Series Regressions and ARIMA) were used.The algorithm was developed in JAVA using up to date forecasting process such as data partition, several error measures and rolling process.Successfully, the results of the algorithm tally with the results of SPSS and Excel.This automatic forecasting will not just benefit forecaster but also end users who do not have in depth knowledge about forecasting techniques

The evaluation of steganography methods on the text domain

One of the main aspects on embedding process of any text steganography methods is the capacity text.It is because a better embedding ratio and saving space offers; a more text can be hidden.This study tries to evaluate several format based techniques of text steganography based on their embedding ratio and saving space capacity.Thus, the main objective of this study is to analyze the performance of text steganography methods which are Changing in Alphabet Letter Patterns (CALP), Vertical based and Quadruple methods based on these two capacity factors.It has been identified that vertical based method give a good effort performance compared to CALP and Quadruple based method.In future, a robustness of text steganography methods should be considered as a next effort in order to find a strength capability on text steganography

Colour feature based analysis on content-based image retrieval techniques

Content-based image retrieval is an active research area that has been famous since 1990s. The main objective of content-based image retrieval is to present users with the most similar images to the search query.This study discusses on the comparative analysis of three techniques frequently used in CBIR which are GLCM, K-Means, and Gabor filtering.The undertaken work focuses on utilizing colour histogram to represent stored images as well as the query. A set of 1000 images that are of 6 categories that includes bus, beach, building, flower, horse, and mountain are used in the analysis.Similarity between an image query and the stored images is calculated based on the Euclidean distance measure. Based on the undertaken experiments, it is learned that the GLCM retrieval model produces a better result as compared to the K-Means and Gabor filtering models

A performance of embedding process for text steganography method

One of the main aspects on embedding process of any text steganography methods is the capacity text.It is because a better embedding ratio and saving space offers; a more text can be hidden. This paper tries to evaluate several format based techniques of text steganography based on their embedding ratio and saving space capacity.Thus, main objective of this paper is to analyze the performance of text steganography methods which are CALP, Vertical based and Quadruple methods based on these two capacity factors.It has been identified that vertical based method give a good effort performance compared to CALP and Quadruple based method.In future, a robustness of text steganography methods should be considered as a next effort in order to find a strength capability on text steganography

Fitness value based evolution algorithm approach for text steganalysis model

In this paper, we present a new alternative method for text steganalysis based on an evolution algorithm, implemented using the Java Evolution Algorithms Package (JEAP).The main objective of this paper is to detect the existence of hidden messages ased on fitness values of a text description.It is found that the detection performance has been influenced by two groups of fitness values which are good fitness value and bad fitness value. This paper provides a valuable insight into the development and enhancement of the text steganalysis domain