On Detecting an Appropriate Model in Time Series Analysis.
Publication Date: 03/07/2024
Author(s): Chukwudi Anderson Ugomma, Emmanuel Uchenna Ohaegbulem.
Volume/Issue: Volume 7 , Issue 2 (2024)
Abstract:
This study sought to present yet another method of decomposition in time series data. The data for this study were of secondary source and obtained from https://ng.www.investing.com/equities/cocacola-bottle-historicaldata which comprised of both the open and the closed stock prices. The two data were firstly tested for randomness and they were confirmed fit for time series analysis. The two data were also subjected to trend curve analysis, and it was observed that both data were of exponential curve since the exponential trend curve exhibited the highest coefficient of determination (88%), among other trend curves which included linear, quadratic, cubic and logarithmic curves. In the decomposition of the two data series, using the exponential trend, it was revealed that the model, for each data were of multiplicative type since the multiplicative model had the Minimum Mean Squared Error (MSE) of 0.00827 and 0.003665 respectively for both Open and Closed Stock Prices of Coca-Cola Data. Hence, in this study, it was recommended that this traditional method of statistics should be applied in the decomposition of any time series data.
Keywords:
Time Series Models, Test of Randomness, Trend Curves, Decomposition of Time Series Models, Mean Squared Error.
