CHAPTER ONE

INTRODUCTION

1.0      INTRODUCTION

Computers will remain an integral part of life; this is as a result of increasing number of areas where they have become indispensible. As a new application emerges computer practitioners are challenged and new systems or applications that address these new identified problems are designed and implemented. Sometimes, post implementation requirements crop up making modifications to already developed applications inevitable or engendering the need for a new application that encompasses all the requirements altogether (Charles, 2001).      

   Worthy of note also is the fact that business decision making relies heavily on market competition, this makes market forecasting very important in business planning. Market forecasting projects future numbers, characteristics and trends in your target market. It is of great importance to business owners, market practitioners, etc.

     In a survey by Dalrymple (1975), he stated that 93 percent of companies indicated that market forecasting was one of the most crucial aspects of their company’s success. Market forecasting can be quite a daunting task for businesses especially small ones as a result of changing consumer preferences, product array and increased competition. They may need to forecast the size and the growth of a market or product category.

  In this project, we are going to develop an intelligent system that forecasts online markets with the aid of statistical models that will help business owners make better business decisions.

1.1    BAGKGROUND OF STUDY

Online marketing have gained in popularity with the FOREX markets top on the list of trades that have been widely utilized. More formally, online marketing refer to any form of trading i.e. buying and selling including advertising that take place over the internet. Online markets are a way of making business more convenient for businesses which may be far away from one another. Through distant communication networks such as telecommunication, sub-sea optical fiber links and web programs over the internet framework these form of marketing have been made possible. In recent times there have been calls to make online marketing more intelligent, in particular helping businesses to survive stiff competition over the internet. We see this as a challenge since there is vast amount of online markets with a heavy presence on the internet.

1.2     STATEMENT OF PROBLEM

Statistical models have been useful in solving a variety of tasks. However, in online marketing forecasts this is yet to be fully realized. Thus, there is need to improve on existing models or invent new ones that can help online markets predict or forecast best market scenarios and avoid huge financial losses.

1.3   OBJECTIVES OF THE STUDY

Our aim in this study is to develop an intelligent system for online market forecasts using statistical model. The objective is to:

             i.                        To improve existing statistical model for intelligently forecasting online market trends.

           ii.                        Provide software interface for monitoring and control of online markets.

        iii.                        To develop a forecasting system that enables business owners predict future business trends.

1.4      SIGNIFICANCE OF THE STUDY

       i.            This study will expand the already rich body of knowledge in online market forecast.

     ii.            It will be useful for businesses on the internet to accurately predict business trends for profit maximization and loss reduction.

 1.5    LIMITATIONS OF THE STUDY

       i.            Time constraint is one of the major challenges incurred by this research work, because to obtain a proper and sophisticated system you need enough time to carry out the research.

     ii.            Inability to obtain adequate information and data, as a result of financial constraint.

1.6     SCOPE OF THE STUDY

       i.            This project will focus on the development of an intelligent online market forecasting system using time series models based on moving averages. This study is limited to online markets of goods and services.

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