A Comparison of Univariate Time Series Methods for Forecasting Cocoa Bean Prices

Kamu, Assis; Ahmed, Amran; Yusoff, Remali; Hassan, A.F.M.K.

doi:10.3923/tae.2010.207.215

Cited by 15 publications

(8 citation statements)

References 2 publications

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“…Two (2) ARIMA models, ARIMA 1,2,2 and ARIMA 2,2,2 were investigated and their model statistics including the adjusted coefficient of determination which indicates the proportion of the variance in the dependent variable (Y) that is predicted or explained by the ARIMA model. Four processes are involved in the use of ARIMA model for Time series data and include identification (of data trends), estimation (of the model coefficient), model checking for efficiency and forecasting (Assis et al, 2010). These 4 processes were employed in the current study.…”

Section: Methodsmentioning

confidence: 99%

Autoregressive Integration Moving Average (ARIMA) Model for Prices of Selected Grains in the South West Nigeria

Dauda

Tiamiyu-Ibrahim

2020

ASD

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Background: Price serves as signals of relative scarcity as well as abundance of a given agricultural product. Prices of agricultural products vary from month to month and even from day to day and this constitutes a source of risk to farmers whose livelihood depends on good pricing of their products.Method: This study was conducted to identify pattern of price dynamics and to develop model for price dynamics of beans, yellow maize and local rice using monthly price data of grains from the National Bureau of Statistics (2006-2015). Result: The rk of the beans followed a regular decline ranging from 0.4035 to 1 while those (rk) of yellow maize and local rice follow non regular decline with the range 0.1448 – 1(yellow maize) and 0.2945 -1 (local rice) thus indicating that the data are non-stationary. The corrected Akaike information criteria (AICC) and other information obtained for the ARIMA1,2,2 were higher than those of ARIMA2,2,2. The AICC obtained for the ARIMA1,2,2 are 958.422 (beans), 1178.169 (yellow Maize) and 1211.778 (local rice). The ARIMA2,2,2 was thence favored above ARIMA1,2,2.

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Section: Methodsmentioning

confidence: 99%

Autoregressive Integration Moving Average (ARIMA) Model for Prices of Selected Grains in the South West Nigeria

Dauda

Tiamiyu-Ibrahim

2020

ASD

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“…As conclusões comparativas sugerem a superioridade do modelo da classe ARIMA; este resultado vai ao encontro dos resultados de Assis et al (2010), em que o modelo ARIMA foi superior ao modelo de alisamento exponencial na previsão dos preços de feijão e de cacau. Com aplicações em mercado financeiro, Newaz (2008) também verifica a superioridade dos modelos ARIMA frente aos modelos de alisamento exponencial.…”

Section: Comparação Entre Os Modelos De Alisamento Exponencial E Da Cunclassified

Modelos univariados de séries temporais para previsão das temperaturas médias mensais de Erechim, RS

Chechi¹,

Bayer

2012

Rev. bras. eng. agríc. ambient.

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R ESU M OEste trabalho apresenta uma análise de séries temporais dos dados de temperatura mínima e temperatura máxima mensal da cidade de Erechim, RS; apresenta-se uma comparação de duas classes de modelos tradicionais de previsão, nomeadamente: modelos da classe ARIMA e modelos de alisamento exponencial. Na classe de modelos ARIMA foram selecionados, utilizando-se critérios de informação, modelos do tipo SARIMA, que consideram a característica sazonal da temperatura do ar; já para os modelos de alisamento exponencial utilizaram-se os modelos Holt-Winters aditivo, em que as constantes de alisamento são determinadas de forma a minimizar o erro quadrático médio entre valores previstos e observados; esta análise permitiu a identificação de componentes como sazonalidade e períodos atípicos. Os modelos de previsão foram comparados para diferentes horizontes de previsão, sendo que os modelos da classe ARIMA se mostraram mais acurados. Os modelos ajustados se mostraram adequados para traçar previsões das variáveis de temperatura do ar, mostrando-se importantes ferramentas para a climatologia agrícola. Palavras-chave: ARIMA, alisamento exponencial, modelos de previsão, sazonalidadeUnivariate time series methods for forecasting the monthly mean air temperature in Erechim, RS A B ST R A C T This paper presents a time series analysis of the minimum and maximum air temperature of Erechim, RS. A comparison between two traditional classes of the forecasting models, namely: ARIMA models and exponential smoothing models is also presented. In the class of ARIMA models using criteria information, SARIMA type models that consider the seasonal characteristics of air temperature were selected, whereas for exponential smoothing models Holt-Winters additive algorithm were used. Smoothing constants are determined to minimize the mean square error between observed and predicted values. This analysis allowed the identification of components such as seasonality and atypical periods. The model predictions were compared for different forecast horizons. The ARIMA class models proved to be more accurate while the adjusted models were adequate for adjusting forecasts of variables of air temperature, being important tools for agricultural climatology.

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“…A time series forecasting methodology was proposed by (Shabri et al, 2009) using artificial neural network and statistical model. A comparison of univariate time series methods was suggested by (Assis et al, 2010) in which they established that the combined ARIMA/ GARCH model outperformed ARIMA and GARCH. Cui et al (2011) developed an algorithm for traffic management in heterogeneous networks.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Linear Prediction Augmented Autoregressive Integrated Moving Average Based Prediction for VBR Video Traffic

Raghuveera¹

2011

Journal of Computer Science

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Problem statement: Network traffic prediction plays a vital role in the optimal resource allocation and management in computer networks. This study introduces an ARIMA based model augmented by Adaptive Linear Prediction (ALP) for the real time prediction of VBR video traffic. The synergy of the two can successfully address the challenges in traffic prediction such as accuracy in prediction, resource management and utilization. Approach: ARIMA application on a VBR video trace results in a component wise representation of the trace which is then used for prediction. The ALP applied afterwards, ensures consistency in error fluctuation and better accuracy in turn. Results: Performance evaluation of the proposed method is carried out using RMSE. The prediction accuracy is improved by 23% and the error variance is reduced by 23%. Conclusion: The performance of the proposed method is thoroughly investigated, by applying it on video traces of different qualities and characteristics.

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A Comparison of Univariate Time Series Methods for Forecasting Cocoa Bean Prices

Cited by 15 publications

References 2 publications

Autoregressive Integration Moving Average (ARIMA) Model for Prices of Selected Grains in the South West Nigeria

Autoregressive Integration Moving Average (ARIMA) Model for Prices of Selected Grains in the South West Nigeria

Modelos univariados de séries temporais para previsão das temperaturas médias mensais de Erechim, RS

Adaptive Linear Prediction Augmented Autoregressive Integrated Moving Average Based Prediction for VBR Video Traffic

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