Application of Holt-Winters Time Series Models
for Predicting Climatic Parameters (Case Study:
Robat Garah-Bil Station, Iran)

Heydari, Mohammad; Ghadim, Hamed Benisi; Rashidi, Majid; Noori, Mohammad

doi:10.15244/pjoes/100496

Cited by 17 publications

(9 citation statements)

References 42 publications

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“…The letters A, M, N, and A d refer to additive, multiplicative, none, and additive damped, respectively ( 29 ). For instance, the ETS (A, A, M) was the method with the additive trend, multiplicative seasonality and additive errors ( 25 , 26 ). By using the state-space structure, the optimal exponential smoothing model could be determined.…”

Section: Methodsmentioning

confidence: 99%

“…Of the several time-series forecast methods demonstrated in the literature (11)(12)(13)(14)(15), the autoregressive integrated moving average (ARIMA) is one of the most commonly used. This method has been extensively used in various domains such as economic (16,17), human medicine (18)(19)(20), veterinary science (21,22), and agriculture science (23)(24)(25). ARIMA is extended by the seasonal autoregressive integrated moving average (SARIMA) method which is suitable for modeling time-series data with a seasonal trend (26)(27)(28).…”

Section: Introductionmentioning

confidence: 99%

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Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

et al. 2021

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Milk production in Thailand has increased rapidly, though excess milk supply is one of the major concerns. Forecasting can reveal the important information that can support authorities and stakeholders to establish a plan to compromise the oversupply of milk. The aim of this study was to forecast milk production in the northern region of Thailand using time-series forecast methods. A single-technique model, including seasonal autoregressive integrated moving average (SARIMA) and error trend seasonality (ETS), and a hybrid model of SARIMA-ETS were applied to milk production data to develop forecast models. The performance of the models developed was compared using several error matrices. Results showed that milk production was forecasted to raise by 3.2 to 3.6% annually. The SARIMA-ETS hybrid model had the highest forecast performances compared with other models, and the ETS outperformed the SARIMA in predictive ability. Furthermore, the forecast models highlighted a continuously increasing trend with evidence of a seasonal fluctuation for future milk production. The results from this study emphasizes the need for an effective plan and strategy to manage milk production to alleviate a possible oversupply. Policymakers and stakeholders can use our forecasts to develop short- and long-term strategies for managing milk production.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

et al. 2021

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“…The approach is popular across disciplines, including hydrology, where HW models have been extensively applied. For example, applications include the forecast of the daily water level in a reservoir [20], rainfall and temperature series predictions [21][22][23], and the forecast of sewage inflow into a wastewater treatment plant [24].…”

Section: Introductionmentioning

confidence: 99%

Comparison between SARIMA and Holt–Winters models for forecasting monthly streamflow in the western region of Cuba

et al. 2021

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The present study aims to compare SARIMA and Holt–Winters model forecasts of mean monthly flow at the V Aniversario basin, western Cuba. Model selection and model assessment are carried out with a rolling cross-validation scheme using mean monthly flow observations from the period 1971–1990. Model performance is analyzed in one- and two-year forecast lead times, and comparisons are made based on mean squared error, root mean squared error, mean absolute error and the Nash–Sutcliffe efficiency; all these statistics are computed from observed and simulated time series at the outlet of the basin. The major findings show that Holt–Winters models had better performance in reproducing the mean series seasonality when the training observations were insufficient, while for longer training subsets, both models were equally competitive in forecasting one year ahead. SARIMA models were found to be more reliable for longer lead-time forecasts, and their limitations after being trained on short observation periods are due to overfitting problems. Article Highlights Comparison based on rolling cross-validation revealed the models forecasts sensibility to available observations amount. HW and SARIMA models perform better when limited observations or long-view forecasting, respectively, otherwise they do similar. HW models were superior modeling less variable monthly flows while SARIMA models better forecast the highly variable periods.

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“…La predicción en temas medioambientales hace que utilicen métodos estadísticos más precisos para estudiar el tiempo y el cambio climático, entre ellos está el modelo de Holt-Winters, el cual puede predecir variables de temperatura y precipitación (Heydari et al, 2020). El modelo de Holt Winters es usado para pronosticar y estimar parámetros del tiempo (Liu & Wu, 2020;Puah et al, 2016;Shah et al, 2018).…”

Section: Introductionunclassified

Pronóstico de precipitaciones mensuales: Un estudio de caso en las comunidades nativas del Perú

et al. 2021

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El análisis de registros de precipitación permite determinar patrones de cambio actuales y retrospectivos, que usados adecuadamente determinarán el potencial de agua de lluvia. El objetivo de la investigación fue pronosticar las precipitaciones mensuales, para la evaluación de instalación de sistemas de captación de agua de lluvia en comunidades nativas de Perú. Se evaluó el comportamiento de las precipitaciones durante los años 2016-2020 con datos del SENAMHI y se determinó los pronósticos de precipitación para el año 2021 y 2022. Se utilizó el modelo aditivo de Holt-Winters para el cálculo de las predicciones. La precipitación promedio durante los 5 años evaluados fue 1 786.24 (mm). El área promedio del techo de las viviendas fue de 47.5 m2, la cantidad de captación de agua fue de 76 361.76 litros anuales/vivienda. Las estimaciones de los pronósticos son constantes, el modelo de tendencia cuadrática arrojó que, para el año 2021 la precipitación se estima en 2 038,804 (mm) y para el año 2022 se estiman 2 217.338 (mm).

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Application of Holt-Winters Time Series Models for Predicting Climatic Parameters (Case Study: Robat Garah-Bil Station, Iran)

Cited by 17 publications

References 42 publications

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

Forecasting of Milk Production in Northern Thailand Using Seasonal Autoregressive Integrated Moving Average, Error Trend Seasonality, and Hybrid Models

Comparison between SARIMA and Holt–Winters models for forecasting monthly streamflow in the western region of Cuba

Pronóstico de precipitaciones mensuales: Un estudio de caso en las comunidades nativas del Perú

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