Project Abstract

Abstract
This research project aims to develop an automated intelligent system for online market forecasts using a statistical model. The system will utilize machine learning algorithms to analyze historical market data and predict future trends with a high level of accuracy. By integrating advanced statistical techniques with artificial intelligence, the system will be able to adapt to changing market conditions and provide real-time forecasts to help investors make informed decisions. The proposed system will leverage big data analytics to process large volumes of information from various online sources, such as social media, news articles, and financial reports. By collecting and analyzing data from multiple channels, the system will be able to identify patterns and correlations that can be used to predict market movements. The use of natural language processing techniques will allow the system to extract valuable insights from unstructured data sources, further enhancing the accuracy of its forecasts. One of the key features of the system is its ability to continuously learn and improve over time. By incorporating feedback mechanisms, the system can refine its models based on new data and market feedback. This adaptive learning approach will enable the system to stay up-to-date with the latest market trends and ensure that its forecasts remain reliable and relevant. In addition to providing accurate forecasts, the system will also offer interactive visualization tools to help users explore and interpret the data. By presenting the information in a clear and intuitive manner, the system will enable users to gain valuable insights into market trends and make better-informed decisions. Furthermore, the system will provide customizable alerts and notifications to keep users informed of significant market developments in real-time. Overall, the development of an automated intelligent system for online market forecasts using a statistical model represents a significant advancement in the field of financial technology. By combining cutting-edge technologies such as machine learning, big data analytics, and natural language processing, the system has the potential to revolutionize the way investors analyze and interpret market data. With its ability to provide accurate forecasts, adaptive learning capabilities, and user-friendly interface, the system promises to empower investors with the tools they need to navigate the complex world of online markets successfully.

Project Overview

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:

1.             i.                       To improve existing statistical model for intelligently forecasting online market trends.
2.           ii.                       Provide software interface for monitoring and control of online markets.
3.         iii.                       To develop a forecasting system that enables business owners predict future business trends.

1.4    SIGNIFICANCE OF THE STUDY

1.       i.           This study will expand the already rich body of knowledge in online market forecast.
2.     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

1.       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.
2.     ii.           Inability to obtain adequate information and data, as a result of financial constraint.

1.6     SCOPE OF THE STUDY

1.        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