Assessing Performance of Estimation Techniques in Time Series Analysis when Trend-cycle Component is Linear
Asian Journal of Research in Computer Science · pp. 18–29 · Published 3 Dec 2024
10.9734/ajrcos/2024/v17i12526Abstract
Abstract: Two decomposition techniques are Buys-Ballot and least square techniques are presented in this study. The two important patterns that may be discussed are trend and seasonality and two competing models are additive and multiplicative models. The trend-cycle component is linear. The emphasis is to assess the performance of Buys-Ballot estimates and least square estimates using accuracy measures (Mean Error (ME), Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results show that the two estimation techniques are very good in estimating the linear trend parameters and seasonal effects when the model for decomposition is additive. It differs for multiplicative model.
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