Comparative study of holt-winters triples exponential smoothing and seasonal Arima: Forecasting short term seasonal car sales in South Africa

Makatjane, Katleho; Moroke, Ntebogang Dinah

doi:10.22495/rgcv6i1art8

Cited by 18 publications

(21 citation statements)

References 18 publications

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“…Yaffee and McGee (2000) have suggested that model parameter estimates must be less than one as to deem them to be sufficient and significant. The same results are also obtained by Makatjane and Moroke (2016) in their study of comparative of Holt-Winters triple exponential smoothing and Seasonal ARIMA models.…”

Section: Results and Analysis Of Sarima Modelsupporting

confidence: 77%

“…Comparative analysis: The purpose of this section is to determine the model which best mimics the data and produces fewer forecasts. With the extension to Makatjane and Moroke (2016) methodology, the current study follows up with the application of four error metrics which are a mean error, mean absolute error, mean percentage error and mean absolute percentage error to measure the performance of each model and results are summarized in Table 3. The results indicate that the model with intervention has the smallest values of all the proposed error metrics.…”

Section: Sarima-intervention Resultsmentioning

confidence: 99%

“…benchmark model with which Makatjane and Moroke (2016) revealed that the multiplicative SARIMA model denoted by SARIMA (p, d, q)x(P, D, Q) S follows this mathematical form:…”

Section: Methodsmentioning

confidence: 99%

“…The hypothesis tested is: H 0 : ρ = 0 H 1 : ρ < 0 also see Mushtaq (2011) and Moroke (2014) respectively. Makatjane and Moroke (2016) have accentuated that the ADF test which was derived by Dickey and Fuller (1979) is used for linear time series models and the test follows some sort of linear regression as: ADF equation with no intercept and no trend:…”

Section: Development Of the Modelmentioning

confidence: 99%

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The Analysis of the 2008 US Financial Crisis: An Intervention Approach

Makatjane¹,

Molefe²,

Wyk³

2018

JEBS

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The current study investigates the impact of the 2008 US financial crises on the real exchange rate in South Africa. The data used in this empirical analysis covers the period from January 2000 to June 2017. The seasonal autoregressive integrated moving average (SARIMA) intervention charter was used to carry out the analysis. Results revealed that the financial crises period in South Africa occurred in March 2008 and significantly affected the exchange rate. Hence, the impact pattern was abrupt. Using the SARIMA model as a benchmark, four error metrics; to be precise mean absolute error (MAE), mean absolute percentage error (MAPE), mean error (ME) and mean percentage error (MPE) was used to assess the performance of the intervention model and SARIMA model. The results of the SARIMA intervention model produced better forecasts as compared to that one of SARIMA model. Furthermore, the results of this empirical analysis provided practical information for the monetary policy committee of SA to make informed policy decisions related to exchange rate movements and we further give sound relevance in using the intervention model to model interrupted time series such as exchange rates. Box et al. (2011) invented two models which are proposed for the current study. These models are known as SARIMA intervention and SARIMA respectively. Additionally, SARIMA model in this study serves as a

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Section: Results and Analysis Of Sarima Modelsupporting

confidence: 77%

Section: Sarima-intervention Resultsmentioning

confidence: 99%

“…benchmark model with which Makatjane and Moroke (2016) revealed that the multiplicative SARIMA model denoted by SARIMA (p, d, q)x(P, D, Q) S follows this mathematical form:…”

Section: Methodsmentioning

confidence: 99%

Section: Development Of the Modelmentioning

confidence: 99%

See 2 more Smart Citations

The Analysis of the 2008 US Financial Crisis: An Intervention Approach

Makatjane¹,

Molefe²,

Wyk³

2018

JEBS

Self Cite

View full text Add to dashboard Cite

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“…According to Carriero et al (2015) forecasting performance is checked to discover the best performing model and utilised the three error measurements; to be specific; mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). In any case, the present study tails these methods of Carriero et al (2015) and Makatjane and Moroke (2016) …”

Section: Forecasting Performance Ofmentioning

confidence: 99%

On the Prediction of the Inflation Crises of South Africa Using Markov-Switching Bayesian Vector Autoregressive and Logistic Regression Models

Makatjane

Moroke

Xaba

2018

Journal of Social Economics Research

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Article HistoryThe aim of this study is to build an early warning system (EWS) model for inflation rates of South Africa (SA). A logistic regression model (LRM) is used in collaboration with a Markov-switching Bayesian vector autoregressive (MS-BVAR) to produce the estimates. Monte Carlo experimental methods are used to simulate both the inflation rate and repo rate of the SA economy. The procedure simulated 228 observations for the period of January 1999 to December 2017. Preliminary results confirmed the applicability of both models for further analyses. MS (2)-BVAR(1) proved to be the most appropriate model for detecting regime shifts in inflation rates. The results indicate that SA inflation might be in a low inflation regime for the period of 11 years and 4 months. Surprisingly, we discovered that the repo rate is not a good tool to combat the inflation rate in SA i.e a 1% increase in the repo in a month significantly increased inflation rate by about 81%. The findings also confirmed 51% and 53% of insample and out-of-sample SA inflation crises forecasts to be correctly classified. This is in accordance with the reported results of Cruz and Mapa (2013) and Makatjane and Xaba (2016). The verdicts of the study are relevant for policy purposes and literature.Contribution/Originality: This study uses new estimation methodology to quantify the likelihood of future inflation crises in South Africa.

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A Sequence to Sequence Long Short-Term Memory Network for Footwear Sales Forecasting

Santos¹,

Matos

Ferreira³

et al. 2022

Intelligent Data Engineering and Automated Learning – IDEAL 2022

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Footwear sales forecasting is a critical task for supporting product managerial decisions, such as the management of footwear stocks and production levels. In this paper, we explore a recently proposed Sequence to Sequence (Seq2Seq) Long Short-Term Memory (LSTM) deep learning architecture for multistep ahead footwear sales Time Series Forecasting (TSF). The analyzed Seq2Seq LSTM neural network is compared with two popular TSF methods, namely ARIMA and Prophet. Using real-world data from a Portuguese footwear company, several computational experiments were held. Focusing on daily sales, we analyze data recently collected during a 3-year period (2019-2021) and related with seven types of products (e.g., sandals). The evaluation assumed a robust and realistic rolling window scheme that considers 28 training and testing iterations, each related with one week of multi-step ahead predictions. Overall, competitive predictions were obtained by the proposed LSTM model, resulting in a weekly Normalized Mean Absolute Error (NMAE) that ranges from 5% to 11%.

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Comparative study of holt-winters triples exponential smoothing and seasonal Arima: Forecasting short term seasonal car sales in South Africa

Cited by 18 publications

References 18 publications

The Analysis of the 2008 US Financial Crisis: An Intervention Approach

The Analysis of the 2008 US Financial Crisis: An Intervention Approach

On the Prediction of the Inflation Crises of South Africa Using Markov-Switching Bayesian Vector Autoregressive and Logistic Regression Models

A Sequence to Sequence Long Short-Term Memory Network for Footwear Sales Forecasting

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