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

Brito, Gustavo Reinel Alonso; Villaverde, Anaily Rivero; Quan, Andrés Lau; Pérez, María Elena Ruiz

doi:10.1007/s42452-021-04667-5

Cited by 14 publications

(5 citation statements)

References 33 publications

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“…It is worth noting that models can have short-term and long-term predictive capabilities, with this study focusing on long-term predictions in line with the definitions of Mosavi et al [71]. The observed errors suggest that SARIMA models tend to be more reliable for longer-term forecasts, in line with the findings of Alonso Brito et al [72]. Additionally, the performance of SARIMA models is highly sensitive to the training time series used in model calibration, as highlighted by Danandeh Mehr et al [35].…”

Section: Discussionsupporting

confidence: 80%

Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Costa,

Menezes Filho,

Canales

et al. 2023

Hydrology

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Stochastic modeling to forecast hydrological variables under changing climatic conditions is essential for water resource management and adaptation planning. This study explores the applicability of stochastic models, specifically SARIMA and SARIMAX, to forecast monthly average river discharge in a sub-basin of the Paranaíba River near Patos de Minas, MG, Brazil. The Paranaíba River is a vital water source for the Alto Paranaíba region, serving industrial supply, drinking water effluent dilution for urban communities, agriculture, fishing, and tourism. The study evaluates the performance of SARIMA and SARIMAX models in long-term discharge modeling and forecasting, demonstrating the SARIMAX model’s superior performance in various metrics, including the Nash–Sutcliffe coefficient (NSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The inclusion of precipitation as a regressor variable considerably improves the forecasting accuracy, and can be attributed to the multivariate structure of the SARIMAX model. While stochastic models like SARIMAX offer valuable decision-making tools for water resource management, the study underscores the significance of employing long-term time series encompassing flood and drought periods and including model uncertainty analysis to enhance the robustness of forecasts. In this study, the SARIMAX model provides a better fit for extreme values, overestimating peaks by around 11.6% and troughs by about 5.0%, compared with the SARIMA model, which tends to underestimate peaks by an average of 6.5% and overestimate troughs by approximately 76.0%. The findings contribute to the literature on water management strategies and mitigating risks associated with extreme hydrological events.

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Section: Discussionsupporting

confidence: 80%

Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Costa,

Menezes Filho,

Canales

et al. 2023

Hydrology

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“…On the other side SARIMA model were found to be more reliable for longer lead-time forecasts. Hence, there is no general conclusion that places the models in a hierarchical order regarding their performance, it depends on the data used in the study [7].…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies on forecasting using these two methods have been conducted by researchers, such as [6], who predicted sales of clothing with seasonal data. In 2021, a comparison between SARIMA and Holt Winter's methods was conducted for the average monthly salary of a company in Cuba [7]. Other studies include forecasting energy consumption for smart grid operations [8], using SARIMA and LSTM on time series data, where the best model among the two methods produced the smallest error.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Seasonal Time Series Forecasting Using Sarima and Holt Winter’s Exponential Smoothing (Case Study: West Sumatra Export Data)

Hasibuan,

Musthofa,

Putri

et al. 2023

BAREKENG: J. Math. & App.

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Export is the activity of selling goods or services from one country to another. This activity usually occurs in a specific region or country. Export data is a type of data that has a seasonal pattern. This study aims to compare SARIMA and Holt Winter’s methods in forecasting export data. In this study, the SARIMA model ((1,1,1) (0,1,1))12 and Holt Winter's simulation were obtained. The data used is the export data of West Sumatra from 2016 to 2022. The best model is the one with the smallest MAPE or MAD. The SARIMA model yielded a MAPE of 0,437% and MAD of 78,821. Meanwhile, the Holt Winter's method yielded a MAPE of 0,894% and MAD of 163,320 with α=0,2, β=0,5, γ=0,1. Therefore, the SARIMA outperformed the Holt Winter’s method due to its higher accuracy. It can be concluded that the SARIMA is suitable to use as the forecasting model in this case. In this study, forecast have been made for the next 24 periods, from January 2023 to December 2024.

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“…Its suitability relies on its accuracy in forecasting, as well as its flexibility and adaptability to a wide range of time series data, such as new car sales. Brito et al [35] concluded that the SARIMA models are more flexible in their application and more accurate in generating quality results.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on New Car Sales in South Africa

Makoni

Chikobvu

2023

Data

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In both developed and developing nations, with South Africa (SA) being one of the latter, the motor vehicle industry is one of the most important sectors. The SA automobile industry was not unaffected by the 2007/2008 global financial crisis (GFC). This study aims to assess the impact of the GFC on new car sales in SA through statistical modeling, an impact that has not previously been investigated or quantified. The data obtained indicate that the optimal model for assessing the aforementioned impact is the SARIMA (0,1,1)(0,0,2)12 model. This model’s suitability was confirmed using Akaike information criterion (AIC) and Bayesian information criterion (BIC), as well as the root mean square error (RMSE) and the mean absolute percentage error (MAPE). An upward trend is projected for new car sales in SA, which has positive implications for SA and its economy. The projections indicate that the new car sales rate has increased and has somewhat recovered, but it has not yet reached the levels expected had the GFC not occurred. This shows that SA’s new car industry has been negatively and severely impacted by the GFC and that the effects of the latter still linger today. The findings of this study will assist new car manufacturing companies in SA to better understand their industry, to prepare for future negative shocks, to formulate potential policies for stocking inventories, and to optimize marketing and production levels. Indeed, the information presented in this study provides talking points that should be considered in future government relief packages.

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

Cited by 14 publications

References 33 publications

Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Comparison of Seasonal Time Series Forecasting Using Sarima and Holt Winter’s Exponential Smoothing (Case Study: West Sumatra Export Data)

Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on New Car Sales in South Africa

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