Application of Seasonal Autoregressive Integrated Moving Average (SARIMA) Method in Forecasting Chicken Egg Prices in Indonesia

Background: Brucellosis runs rampant endemically with sporadic outbreaks in Algeria. The present study aimed to provide insights into the epidemiology of brucellosis and compare the performance of some prediction models using surveillance data from Tebessa province, Algeria. Study Design: A retrospective study.Methods: Seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly brucellosis notifications. The prediction performance of these models was compared using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Results: Overall, 13670 human brucellosis cases were notified in Tebessa province from 2000-2020 with a male-to-female ratio of 1.3. The most affected age group was 15-44 years (56.2%). The cases were reported throughout the year with manifest seasonality. The annual notification rate ranged from 30.9 (2013) to 246.7 (2005) per 100000 inhabitants. The disease was not evenly distributed, rather spatial and temporal variability was observed. The SARIMA (2,1,3) (1,1,1)12', NNAR (12,1,6)12' , and SARIMA (2,0,2) (1,1,0)12- NNAR (5,1,4)12 were selected as the best-fitting models. The RMSE, MAE, and MAPE of the SARIMA and SARIMA-NNAR models were by far lower than those of the NNAR model. Moreover, the SARIMA-NNNAR hybrid model achieved a slightly better prediction accuracy for 2020 than the SARIMA model. Conclusion: As evidenced by the obtained results, both SARIMA and hybrid SARIMA-NNAR models are suitable to predict human brucellosis cases with high accuracy. Reasonable predictions, along with mapping brucellosis incidence, could be of great help to veterinary and health policymakers in the development of informed, effective, and targeted policies, as well as timely interventions.

Prediksi deret waktu pada parameter data cuaca adalah proses memprediksi nilai masa depan berdasarkan pola data historis cuaca. Penelitian ini mengatasi kelemahan penelitian sebelumnya seperti data yang terbatas, jangka waktu prediksi, keterbatasan parameter yang digunakan dalam penelitian serta tidak menggunakan parameter eksternal yang tentunya dapat membantu proses prediksi model menjadi lebih akurat. Penelitian ini menggunakan metode Long Short-Term Memory (LSTM) dan Seasonal AutoRegressive Integrated Moving Average (SARIMA) untuk memprediksi parameter cuaca, seperti tekanan udara, suhu, dan kelembaban relatif, dengan pendekatan single-step dan multi-step ahead. Data bersumber dari BMKG Stasiun Meteorologi Pinangsori, Sibolga, selama 8 tahun, dengan granularity per jam dan per hari. Hasil eksperimen menunjukkan bahwa LSTM dan SARIMA, memiliki keunggulan dalam konteks tertentu. Untuk pendekatan single-step, model SARIMA lebih baik 49% dari model LSTM untuk prediksi dengan granularity data per jam. Namun, untuk granularity data per hari, performansi LSTM lebih baik 27% dari model SARIMA. Kemudian untuk pendekatan multi-step, SARIMA memberikan performansi yang lebih baik 30% daripada model LSTM untuk data per jam (< =24). Sedangkan untuk granularity data harian, model LSTM lebih baik 30% pada step 30 hari dan lebih baik 27% pada step 60 hari. Dan untuk data ekstrem, LSTM lebih baik 47% daripada SARIMA. Abstract Time series prediction of weather data parameters involves forecasting future values based on historical weather patterns. This study addresses the limitations of previous research, such as constrained datasets, short prediction periods, restricted parameters, and the neglect of external factors that could enhance model accuracy. Utilizing Long Short-Term Memory (LSTM) networks and Seasonal AutoRegressive Integrated Moving Average (SARIMA) methods, the research focuses on predicting weather parameters like air pressure, temperature, and relative humidity using both single-step and multi-step approaches. The data is sourced from the BMKG Pinangsori Meteorological Station in Sibolga, covering an 8-year period with both hourly and daily granularity. The experimental findings reveal that LSTM and SARIMA each have their advantages depending on the context. In the single-step approach, the SARIMA model outperforms the LSTM model by 49% for predictions based on hourly data. Conversely, for daily data granularity, the LSTM model surpasses SARIMA by 27%. In the multi-step analysis, SARIMA demonstrates a 30% improvement over LSTM for hourly predictions (up to 24 hours). However, for daily granularity, the LSTM model excels, showing a 30% advantage at the 30-day prediction step and a 27% advantage at the 60-day step. Additionally, LSTM significantly outperforms SARIMA by 47% when dealing with extreme data.

Prediksi deret waktu pada parameter data cuaca adalah proses memprediksi nilai masa depan berdasarkan pola data historis cuaca. Penelitian ini mengatasi kelemahan penelitian sebelumnya seperti data yang terbatas, jangka waktu prediksi, keterbatasan parameter yang digunakan dalam penelitian serta tidak menggunakan parameter eksternal yang tentunya dapat membantu proses prediksi model menjadi lebih akurat. Penelitian ini menggunakan metode Long Short-Term Memory (LSTM) dan Seasonal AutoRegressive Integrated Moving Average (SARIMA) untuk memprediksi parameter cuaca, seperti tekanan udara, suhu, dan kelembaban relatif, dengan pendekatan single-step dan multi-step ahead. Data bersumber dari BMKG Stasiun Meteorologi Pinangsori, Sibolga, selama 8 tahun, dengan granularity per jam dan per hari. Hasil eksperimen menunjukkan bahwa LSTM dan SARIMA, memiliki keunggulan dalam konteks tertentu. Untuk pendekatan single-step, model SARIMA lebih baik 49% dari model LSTM untuk prediksi dengan granularity data per jam. Namun, untuk granularity data per hari, performansi LSTM lebih baik 27% dari model SARIMA. Kemudian untuk pendekatan multi-step, SARIMA memberikan performansi yang lebih baik 30% daripada model LSTM untuk data per jam (< =24). Sedangkan untuk granularity data harian, model LSTM lebih baik 30% pada step 30 hari dan lebih baik 27% pada step 60 hari. Dan untuk data ekstrem, LSTM lebih baik 47% daripada SARIMA. Abstract Time series prediction of weather data parameters involves forecasting future values based on historical weather patterns. This study addresses the limitations of previous research, such as constrained datasets, short prediction periods, restricted parameters, and the neglect of external factors that could enhance model accuracy. Utilizing Long Short-Term Memory (LSTM) networks and Seasonal AutoRegressive Integrated Moving Average (SARIMA) methods, the research focuses on predicting weather parameters like air pressure, temperature, and relative humidity using both single-step and multi-step approaches. The data is sourced from the BMKG Pinangsori Meteorological Station in Sibolga, covering an 8-year period with both hourly and daily granularity. The experimental findings reveal that LSTM and SARIMA each have their advantages depending on the context. In the single-step approach, the SARIMA model outperforms the LSTM model by 49% for predictions based on hourly data. Conversely, for daily data granularity, the LSTM model surpasses SARIMA by 27%. In the multi-step analysis, SARIMA demonstrates a 30% improvement over LSTM for hourly predictions (up to 24 hours). However, for daily granularity, the LSTM model excels, showing a 30% advantage at the 30-day prediction step and a 27% advantage at the 60-day step. Additionally, LSTM significantly outperforms SARIMA by 47% when dealing with extreme data.

This research used a time series model called the Seasonal Autoregressive Integrated Moving Average (SARIMA) technique to model and forecast wholesale prices of Tanzania`s key food crops, notably maize, rice, and beans. The SARIMA model was selected due to its ability of fitting data with seasonality. Monthly wholesale prices data of the three crops between February 2004 to October 2021 in Tanzania were retrieved from the website of the Bank of Tanzania (BoT), resulting in 213 observations on each crop. The data from February 2004 to October, 2020 were used to fit a SARIMA model and data of November 2020 to October 2021 were used to validate the model. The results show that SARIMA (0,1,2) (1,0,1)12, SARIMA (0,1,0) (1,1,1)12 and SARIMA (0,1,0) (0,1,1)12 are the most suitable models for forecasting wholesale prices of maize, rice and beans respectively. The model’s accuracy was tested using Mean Absolute Percent Error (MAPE), and the results were found satisfactory. The results reveal that maize, rice, and beans will all have higher peak prices in February 2022, with TZS 54,083/=, TZS 167,258/=, and TZS 180,117.68/= per 100kg, respectively. Therefore, SARIMA (0,1,2)(1,0,1)12, SARIMA (0,1,0)(1,1,1)12 and SARIMA (0,1,0) (0,1,1)12 models could serve as a useful tool for modelling and forecasting monthly wholesale prices of maize, rice and beans respectively in Tanzania.

Developing an accurate forecasting model for electricity demand plays a vital role in maximising the efficiency of the planning process in the power generation industries. The time series data of electricity demand in Malaysia is highly volatile with seasonal characteristics. This study aims to evaluate the forecasting performance of the seasonal autoregressive integrated moving average (SARIMA) model with GARCH for weekly maximum electricity demand. The weekly maximum electricity demand data (in megawatt, MW) from 2005 to 2016 has been used for this study. The results show that SARIMA(1, 1, 0)(0, 1, 0)52−GARCH(1, 2) with generalized error distribution (GED) is the most appropriate model for forecasting electricity demand due to its parsimonious characteristic with low values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) which are 644.1828, 523.8380 and 3.13%, respectively. The MAPE value of the proposed model which is less than 5% indicates that the SARIMA − GARCH model is relatively good in forecasting electricity demand for the case of Malaysia data. In conclusion, the proposed model of SARIMA with GARCH has great potential and provides a promising performance in forecasting electricity demand with seasonal highly volatile characteristics.

A commonly used data assimilation (DA) algorithm, Kalman filter, is integrated with the seasonal autoregressive integrated moving average (SARIMA) model to make a one-step forecast of monthly municipal solid waste (MSW) generation in Seattle. The DA solves the problem that parameters of the forecasting model need to be updated in every forecasting process. The performances of prediction models are compared using mean absolute percentage error (MAPE), root-mean-square-error (RMSE), and 95% confidence interval. The MAPE of the SARIMA model with DA is 0.0422, whereas the MAPE of the SARIMA without DA is 0.0914. A 95% confidence interval of SARIMA without DA keeps increasing, whereas SARIMA with DA remains constant, which means DA raises the stability of SARIMA as time progresses. Results show that DA enables the same MSW prediction model with more accurate and more robust forecast results. The SARIMA parameter updating cycle can be prolonged, which saves time and effort.

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 is for 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.

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 is for 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.

Milk production in the Andean highlands is variable over space and time. This variability is related to fluctuating environmental factors such as rainfall season which directly influence the availability of livestock feeding resources. The main aim of this study was to develop a time-series model to forecast milk production in a mountainous geographical area by analysing the dynamics of milk records thorough the year. The study was carried out in the Andean highlands, using time–series models of monthly milk records collected routinely from dairy cows maintained in a controlled experimental farm over a 9-year period (2008–2016). Several statistical forecasting models were compared. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE) were used as selection criteria to compare models. A relation between monthly milk records and the season of the year was modelled using seasonal autoregressive integrated moving average (SARIMA) methods to explore temporal redundancy (trends and periodicity). According to white noise residual test (Q = 13.951 and p = 0.052), Akaike Information Criterion and MAE, MAPE, and RMSE values, the SARIMA (1, 0, 0) x (2, 0, 0)12 time-series model resulted slightly better forecasting model compared to others. In conclusion, time-series models were promising, simple and useful tools for producing reasonably reliable forecasts of milk production thorough the year in the Andean highlands. The forecasting potential of the different models were similar and they could be used indistinctly to forecast the milk production seasonal fluctuations. However, the SARIMA model performed the best good predictive capacity minimizing the prediction interval error. Thus, a useful effective strategy has been developed by using time-series models to monitor milk production and alleviate production drops due to seasonal factors in the Andean highlands.

Rainfall estimates are important components of water resources applications, especially in agriculture, transport constructing irrigation and drainage systems. This paper aims to stochastically model and forecast the rainfall trend and pattern for a city, each purposively selected in five states of the South-Western Region of Nigeria. The data collected from Nigerian Meteorological Agency (NIMET) website are captured with fractional autoregressive integrated moving average (ARFIMA) and seasonal autoregressive integrated moving average (SARIMA) models. The autocorrelation function (ACF) and partial autocorrelation function (PACF) are used for model identification, the models selected are subjected to diagnostic checks for the models adequacy. Several tests: Augmented Dickey Fuller (ADF), Ljung Box and Jarque Bera tests are used for investigating unit root, serial autocorrelation and normality of residuals, respectively; the mean square error, root mean square error and mean absolute error are employed in validating the optimal stochastic model for each city in all states, in which the model with the lowest error of forecasting of all competing models is suggested as the best. The analyses and findings suggest SARIMA(1,0,1)(1,1,0) [12], SARIMA(3,0,2)(1,0,0) [12], SARIMA(1,0,0)(1,1,0) [12], SARIMA(2,0,2)(2,1,0) [12] and SARIMA(0,0,1)(1,1,0) [12] for (Ibadan) Oyo State, (Ikorodu) Lagos State, (Osogbo) Osun State, (Abeokuta) Ogun State and (Akure) Ondo state, respectively. The seasonal ARIMA (SARIMA) model was proven to be the best optimal stochastic forecast model for forecasting rainfall in the selected cities. The SARIMA model was, therefore, recommended as a veritable technique that will assist decision makers (Government, Farmers, and Policymakers) to establish better strategies “aprior” on the management of rainfall against upcoming weather changes to ensure increase in agricultural yields for the betterment of the citizenry and general economic growth.

This study conducts a comprehensive empirical analysis of modeling and forecasting the daily stock prices of PZ Cussons Nigeria PLC using the Seasonal Autoregressive Integrated Moving Average (SARIMA) framework within the Box-Jenkins methodology. Utilizing a dataset of 2,289 daily closing prices spanning from January 5, 2015, to November 22, 2023, the research rigorously compares the forecasting performance of SARIMA against a non-seasonal ARIMA benchmark. Stationarity is assessed via the Augmented Dickey-Fuller (ADF) test, with model selection guided by the Akaike Information Criterion (AIC). The optimal models identified are SARIMA(1,0,1)(0,3,2,20) and ARIMA(5,3,0), with diagnostic checks confirming adequacy through residual analysis and Ljung-Box tests. Forecast accuracy is evaluated out-of-sample using root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) across multiple horizons. Results indicate that while ARIMA exhibits marginally lower RMSE in short-term forecasts, SARIMA consistently outperforms in MAPE and demonstrates superior robustness over medium to long-term horizons, particularly beyond one month. Trading strategy backtests further reveal that SARIMA-based signals yield significantly higher cumulative returns (15.5% vs. 8.1%), hit rates (56% vs. 47%), and Sharpe ratios (1.12 vs. 0.68), with lower maximum drawdowns. These findings underscore the critical role of seasonality potentially driven by institutional rebalancing, fiscal cycles, and behavioral market responses in Nigerian equity price dynamics. The study contributes to the literature on financial econometrics in emerging African markets by providing empirical evidence that SARIMA offers enhanced predictive accuracy and practical trading utility compared to traditional ARIMA, affirming the relevance of classical time series models in contemporary quantitative finance.

The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). This study utilizes data from two wind farms located in Poland—‘Gizałki’ and ‘Łęki Dukielskie’—to exclude the possibility of biased results due to specific characteristics of a single farm and to allow for a more comprehensive comparison of the effectiveness of both time series analysis methods. Model parameterization was optimized through a grid search based on the Mean Absolute Percentage Error (MAPE). The performance of the best models was evaluated using Mean Bias Error (MBE), MAPE, Mean Absolute Error (MAE), and R2Score. For the Gizałki farm, the ARIMA model outperformed SARIMA and SVR, while for the Łęki Dukielskie farm, SARIMA proved to be the most accurate, highlighting the importance of optimizing seasonal parameters. The SVR method demonstrated the lowest effectiveness for both datasets. The results indicate that the ARIMA and SARIMA models are effective for forecasting wind farm energy production. However, their performance is influenced by the specificity of the data and seasonal patterns. The study provides an in-depth analysis of the results and offers suggestions for future research, such as extending the data to include multidimensional time series. Our findings have practical implications for enhancing the accuracy of wind farm energy forecasts, which can significantly improve operational efficiency and planning.

Objective: To determine the potential effect of environment variables on cutaneous leishmaniasis occurrence using time-series models and compare the predictive ability of seasonal autoregressive integrated moving average (SARIMA) models and Markov switching model (MSM). Methods: This descriptive study employed yearly and monthly data of 49 364 parasitologically-confirmed cases of cutaneous leishmaniasis in Isfahan province, located in the center of Iran from January 2000 to December 2019. The data were provided by the leishmaniasis national surveillance system, the meteorological organization of Isfahan province, and Iranian Space Agency for vegetation information. The SARIMA and MSM models were implemented to examine the environmental factors of cutaneous leishmaniasis epidemics. Results: The minimum relative humidity, maximum relative humidity, minimum wind speed, and maximum wind speed were significantly associated with cutaneous leishmaniasis epidemics in different lags (P<0.05). Comparing SARIMA and MSM, Akaikes information criterion (AIC), and mean absolute percentage error (MAPE) in MSM were much smaller than SARIMA models (MSM: AIC=0.95, MAPE=3.5%; SARIMA: AIC=158.93, MAPE:11.45%). Conclusions: SARIMA and MSM can be a useful tool for predicting cutaneous leishmaniasis in Isfahan province. Since cutaneous leishmaniasis falls into one of two states of epidemic and non-epidemic, the use of MSM (dynamic) is recommended, which can provide more information compared to models that use a single distribution for all observations (Box-Jenkins SARIMA model).

Rescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models. In this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R 2) as performance metrics. In this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R 2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R 2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R 2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R 2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA's predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions. While the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

The rising sea levels can threaten millions of people residing along the coast or lowlands. The risk can be mitigated by the sea-level prediction done by collecting information on the likelihood of rising sea levels. The Ministry of Marine Affairs and Fisheries of Indonesia has developed Perangkat Ukur Murah untuk Muka Air Laut (Inexpensive Device for Sea Level Measurement, PUMMA) to measure sea levels. PUMMA is located in remote monitoring stations based on Indonesian maritime area. The PUMMA system currently lacks a prediction feature. This objective of this study is to model the sea-level prediction using the dataset for one year, from July 2021 until July 2022. The seasonal autoregressive integrated moving average (SARIMA) method was used because SARIMA proved to be a flexible and versatile method for a dataset having noncomplex nature and seasonal patterns. This study has developed several models of the SARIMA. The model performance was evaluated using the mean absolute percentage error (MAPE), R-squared, mean square error (MSE), and root mean square error (RMSE) metrics. The SARIMA(1, 1, 0)(1, 1, 1)12 model achieved the lowest prediction error with an R-squared of 0.508, MSE of 0.0479, and RMSE of 0.069. Based on the performance, SARIMA(1, 1, 0)(1, 1, 1)12 model is feasible for predicting sea levels using the PUMMA dataset.