Thesis Abstract

Time series analysis is a crucial statistical technique used to analyze and interpret data points collected sequentially over time. This research project delves into the various methods and applications of time series analysis in different fields such as finance, economics, weather forecasting, and more. The primary focus of this study is to explore the predictive capabilities of time series analysis, allowing researchers and practitioners to make informed decisions based on historical data patterns. The research starts by introducing the fundamental concepts of time series analysis, including trend analysis, seasonality, and autocorrelation. These concepts serve as the building blocks for more advanced time series models, such as autoregressive integrated moving average (ARIMA) and exponential smoothing methods. By understanding these foundational principles, analysts can effectively model and forecast future trends in time series data. Furthermore, the research explores the practical applications of time series analysis in real-world scenarios. In finance, for example, time series analysis is commonly used to predict stock prices, commodity trends, and exchange rates. By applying statistical techniques to historical market data, investors can identify patterns and make strategic investment decisions. Similarly, in meteorology, time series analysis is employed to forecast weather patterns, track climate changes, and predict natural disasters. By analyzing historical weather data, meteorologists can anticipate extreme weather events and issue timely warnings to the public. Moreover, the research delves into the importance of time series analysis in economic forecasting. By examining past economic indicators such as GDP, inflation rates, and unemployment figures, economists can predict future economic trends and formulate policies to mitigate potential risks. Time series models play a vital role in understanding the cyclical nature of economic variables and projecting future outcomes based on historical data patterns. In conclusion, this research project highlights the significance of time series analysis as a powerful tool for data interpretation and prediction. By leveraging statistical methods and mathematical models, analysts can extract valuable insights from time series data and make informed decisions in various fields. Whether used for financial forecasting, weather prediction, or economic analysis, time series analysis provides a systematic approach to understanding sequential data and uncovering underlying patterns. As technology continues to advance, the applications of time series analysis are expected to expand, offering new opportunities for data-driven decision-making and strategic planning.

Thesis Overview

GENERAL INTRODUCTION

Time series is very important in business analysis, and it enables us to know the estimate of buyers’ demand for the product or service. Time series is different from random samples.

This is true particularly of certain set of economic data such as the cost of living or the consumption of alcohol. Statistical techniques cannot be applied to such data. The aim of this project is to look at method to treat data of this sort and one such method is that of Time Series. Time Series analysis helps to know the future (that is forecasting) has become more important issue in today’s world business environment. Time Series helps deal with the statistical techniques of analyzing past data and projecting them to obtain estimate to future value.

The basic purpose of time series analysis is to give management a convenient method of measuring changes in the business over a period of time and relating these changes to those in the economy. Time Series that measure changes in one’s own business are supplied by the internal records of the company, while information on changes in the whole industry and in business in general will come from various external sources. The special methods of time series analysis will be given detailed treatment in the following chapters.

DEFINITION OF TIME SERIES

A TIME SERIES is a set of observation obtained by measuring a single variable regularly over a period of time. Observation of the variable are usually recorded at equally spaced part in time. For example, the number of patients treated for malaria on monthly basis, daily consumption of electricity etc.

1.2 DEFINITION OF FORECAST

Forecast means prediction that involves explaining events which will occur at some future time. And the process of arriving at such explanation is called forecasting.

1.3 AIMS AND OBJECTIVES OF THE STUDY

The purpose of this study is to carry out the statistical analysis on the sales of forecasting of wheat flour product in Edo State Flour Mill Company Plc for the next two (2) years and ten (10) years sales from…

To be able to know the changes in sales of flour mill per year.

Describing the time series in concise way.

Examining the behaviour of the series.

Forecasting the behaviour of the series in the future.

1.4 SCOPE AND COVERAGE OF THE STUDY

The study shall be an over view of time series and its analysis and it does not intend to go beyond the subject and all statement and expression are being focused on time series. It encompasses the roles, importance, historical background and types of time series and above all forecasting. Emphasis shall be on time series analysis with respect to forecasting. This project work also seek to analyze the previous ideals put above outline objectives.

The material in this work is divided into six chapters. Chapter one deals with the introduction, definition of time series and forecast as well as aims and objectives. Chapter two focuses on the literature review of the subject matter by showing work done by several authors over time. Chapter three deals with trend analysis and its definition reason for studying trends and also ways to measure the components of time series.

Chapter four deals with trend analysis and its definition, reason for studying trends and also ways to measure the components of time series. Chapter five talks about the analysis of the data collected, while chapter six summarizes and concluded the whole work.

1.5 LIMITATIONS AND PROBLEM OF THE STUDY

This basically remains the search for data due to the fact that there was the problem of not at- office by the workers. However, after a series of call backs, I was able to achieve my aims.

It should be clear that this work is purely on academic exercises, and it should be noted that the limitations had no serious effect on this project work.