Uses of the Buys-Ballot Table in Time Series Analysis

Iwueze, Iheanyi S.; Nwogu, Eleazar C.; Ohakwe, J.; Ajaraogu, Jude C.

doi:10.4236/am.2011.25084

Cited by 22 publications

(23 citation statements)

References 8 publications

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“…The time plot of other series not shown in this paper follows similar pattern when the series is multiplicative. Figure 4 below show the Buys-Ballot method propose by Iwueze et al [12]. From the plot below, the seasonal standard deviation shows appreciable increase/decrease with respect to increase/decrease in the seasonal means.…”

Section: Simulations Of Both Additive and Multiplicative Seriesmentioning

confidence: 99%

“…Chatfield [5] noted that if the seasonal variation stays roughly the same size regardless of the mean level, then it is additive but if it increases in size in direct proportion to the mean level, the appropriate model for decomposition is the multiplicative models. A better interpretation of the methods of Brockwell and Davis, and Chatfield [2], [5] is given in Iwueze et al [12]. Instead of using the seasonal pattern as display by the time series plot of the entire series, they used the relationship between the plot of the seasonal means and seasonal standard deviation derived from the Buys-Ballot Table (see Table 1).…”

Section: Buys-ballot Representation Of Seasonal Time Seriesmentioning

confidence: 99%

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Methods for choice of model in descriptive time se-ries : A review with example

Enegesele

Iwueze

Ijomah

et al. 2017

IJASP

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This paper is a review of existing methods with illustrative examples for choice of model in descriptive time series. The methods reviewed are three graphical methods, X-12 ARIMA method and the method of seasonal differences and quotients. For each method, the same illustrative example and simulated data were used to demonstrate each method. The results obtained from the various methods using simulated additive and multiplicative series show that the various methods identified correctly the appropriate model for decomposition. Also, applying the various methods to quarterly sales of petroleum products from 2004-2013, the result reviewed that the appropriated model for decomposition of the series is the multiplicative model.

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Section: Simulations Of Both Additive and Multiplicative Seriesmentioning

confidence: 99%

Section: Buys-ballot Representation Of Seasonal Time Seriesmentioning

confidence: 99%

Methods for choice of model in descriptive time se-ries : A review with example

Enegesele

Iwueze

Ijomah

et al. 2017

IJASP

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show abstract

“…For quarterly data, we would expect to see significant spikes at lag 4,8,12,16, and so on. Iwueze, Nwogu, Ohakwe, and Ajaraogu (2011) pointed out that seasonality in time series can be identified from the time plot of the entire series by regularly spaced peaks and troughs which have a consistent direction and approximately the same magnitude every period/year, relative to the trend.…”

mentioning

confidence: 99%

Some Tests for Seasonality in Time Series Data

Nwogu¹,

Iwueze²,

Nlebedim³

2016

J. Mod. App. Stat. Meth.

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“…Additive model (when trend, seasonal and cyclical components are additively com- [15] showed that the row and column averages and variances can be used to 1) determine the need for and appropriate trans- …”

Section: Introductionmentioning

confidence: 99%

Comparison of Two Time Series Decomposition Methods: Least Squares and Buys-Ballot Methods

Iwueze¹,

Nwogu²,

Nlebedim³

et al. 2016

OJS

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This paper discusses comparison of two time series decomposition methods: The Least Squares Estimation (LSE) and Buys-Ballot Estimation (BBE) methods. As noted by Iwueze and Nwogu (2014), there exists a research gap for the choice of appropriate model for decomposition and detection of presence of seasonal effect in a series model. Estimates of trend parameters and seasonal indices are all that are needed to fill the research gap. However, these estimates are obtainable through the Least Squares Estimation (LSE) and Buys-Ballot Estimation (BBE) methods. Hence, there is need to compare estimates of the two methods and recommend. The comparison of the two methods is done using the Accuracy Measures (Mean Error (ME)), Mean Square Error (MSE), the Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). The results from simulated series show that for the additive model; the summary statistics (ME, MSE and MAE) for the two estimation methods and for all the selected trending curves are equal in all the simulations both in magnitude and direction. For the multiplicative model, results show that when a series is dominated by trend, the estimates of the parameters by both methods become less precise and differ more widely from each other. However, if conditions for successful transformation (using the logarithmic transform in linearizing the multiplicative model to additive model) are met, both of them give similar results.

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Uses of the Buys-Ballot Table in Time Series Analysis

Cited by 22 publications

References 8 publications

Methods for choice of model in descriptive time se-ries : A review with example

Methods for choice of model in descriptive time se-ries : A review with example

Some Tests for Seasonality in Time Series Data

Comparison of Two Time Series Decomposition Methods: Least Squares and Buys-Ballot Methods

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