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Time Series Analysis On Consumption Of Electricity In Kwara State
[A CASE STUDY OFFA NEPA DISTRICT OFFICE FROM 2001-2015] -
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2.7 COMPONENT/CHARACTERISTICS OF TIME SERIES
According to Murray .R. Spiegel Metric Edition 1961, there are four characteristics or component of time series.
When these types of data is regarded by days, month or year, we would find out that the variable under investigation is fluctuating from time to time, the change or fluctuation are caused by composite force that is constantly at work. The force has four components or features namely;
A trend (or secular trend)
Seasonal variation (or fluctuation)
Cyclical variation
Irregular variation or random
2.8 ANALYSIS OF TIME SERIES
The analysis is usually used to detect the pattern of changes in statistical information over regular interval of time.
These patterns are projected to arrive at an estimate for the future. The time series analysis helps us to cope with future uncertainty.
SECULAR OR TREND: The trend is the general direction in which the time plot appears to be moving. It is denoted T_t, it is said to be containing trend if the mean of X_t varies systematically with time, the systematic change could be linear, quadratic, exponential or function in form.
(2) SEASONAL VARIATION: These are short term variation due to different circumstance that may prevail and effect result at certain period of year or days of the week, time of the day.
A series is said to have seasonal if it exhibit similar or identical pattern for corresponding period of successive year. Such movement are due to recurring event which take place annually.
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ABSRACT - [ Total Page(s): 1 ]ABSTRACT HERE ... Continue reading---
TABLE OF CONTENTS - [ Total Page(s): 1 ]TABLE OF CONTENTCOVER PAGE APPROVAL PAGE DEDICATION ACKNOWLEDGEMENTTABLE OF CONTENT1.1 INTRODUCTION1.2 BACKGROUND AND ITS OPERATION 1.3 SIGNIFICANCE1.4 SCOPE OF STUDY1.5 AIMS AND OBJECTIVE 1.6 PROBLEMS AND LIMITATION OF STUDY 1.7 DEFINITION OF TERMS 1.8 ABBREVIATION USEDCHAPTER TWO 2.0 LITERATURE REVIEW2.1 METHOD OF DATA ANALYSIS 2.2 TIME SERIES ANALYSIS 2.3 IMPORTANCE OF TIME SERIES 2.4 NATURE OF TIME SERIES 2.5 ... Continue reading---
CHAPTER ONE - [ Total Page(s): 2 ]CHAPTER ONE1.0 INTRODUCTION The primary aim of National Electrical Power Authority (NEPA) is in cardinal point which is to generate, transmission, distribution and sales electricity within and outside country. National Electric Power Authority NEPA can be regarded as heart beat of the nation economy as the operation of machines use in industries and most household equipment depend on electricity. Today National Electric Power Authority (NEPA) meets total maximum energy demand from t ... Continue reading---
BIBLIOGRAPHY - [ Total Page(s): 1 ]BIBLIOGRAPHYFreud J.E. William F.J (1970) “Modern Business Statistics†Pitman Publishing bid great Britain Murray .R, Spiegal (1961) “Theory and problems of Statistics†in S.I (Schaum’s outline series)Notational Electric Power Authority 2003 Press Clip (NEPA transformation newsletter Vol.0033) J.B BABALOLAJ.B BABALOLA “Statistics with applications (in behavioural Science, Business and Engineering) Revised Editions MR. .I.O Azeez “ ... Continue reading---
CHAPTER THREE - [ Total Page(s): 12 ]SEMI-MOVING AVERAGE METHOD This consist of separating the data parts (preferably equal) averaging the data in each part, this obtaining two points on the graph of the time points and the trend value can be determined directly with out a graph.FREE HAND METHOD This is method which consist of fitting a trend line or curve simply by looking at the graph, can be used to estimate T trend. ESTIMATION OF SEASONAL VARIATION There are different methods available for computing seasona ... Continue reading---
CHAPTER FOUR - [ Total Page(s): 10 ]Since we must eliminate seasonal variation, the graph label Fig. 11. The seasonal variation in our time series and it is used to remove the effect of seasonal from time series which is called de-seasonalizing a time series. This graph made us proceed be deseasonilize our data as table 4.4 Moving average graph and original data of the montly consumption from 1998-2003 The graph of the original data shows in the figure by the solid line graph of the moving average is shown in broken ... Continue reading---
CHAPTER FIVE - [ Total Page(s): 1 ]CHAPTER FIVE5.1 SUMMARY OF FINDINGS The success of any analysis can only but judged by extent to which to which the objectives of the research have been achieved. While the series contain the upward trend figure 1 and 2 shows it to be very week one, the same conclusion can be drawn about the seasonal factor, these shows in table 4:3. However a different conclusion can be drawn about = periodic cyclical reregulation factor. By residual reasoning, it can be inferred that ... Continue reading---
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ABSRACT - [ Total Page(s): 1 ]ABSTRACT HERE ... Continue reading---