Analisis Prediksi Debit Sungai Amprong Dengan Model Arima (Autoregressive Integrated Moving Average) Sebagai Dasar Penyusunan Pola Tata Tanam

Rahayu, Wiwin Sri; Juwono, Pitojo Tri; Soetopo, Widandi

doi:10.21776/ub.pengairan.2019.010.02.04

Cited by 9 publications

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“…The exponential smoothing models are generated on the basis of the trend and seasonality found in the time-series data. ARIMA models, on the other hand, are mainly generated based on the autocorrelations in the data [20,21]. In this study, an exponential smoothing model and an ARIMA model are built for each station.…”

Section: Analysis Of Forecasting Modelsmentioning

confidence: 99%

“…There are three components of ARIMA, namely: the autoregressive (AR) components, the moving average models (MA) ones, and the integrated (I) ones. A general expression of an ARIMA model is ARIMA (p, d, q), in which p expresses the autoregressive order, d expresses the integrated order, and q expresses the moving-average order [20,21]. An autoregressive (AR) model of the order p is the one in which the current observation 𝑦 𝑡 is regressed on previous observations, represented in equation( 8)…”

Section: Autoregressive Integrated Moving Average (Arima) Modelsmentioning

confidence: 99%

“…The integration is the inverse of differencing. The entire model can be written as in equation ( 10) [20]: (10) where yt′ denotes the truncated series. Both lagged 𝑦 𝑡 values and lagged errors are included in the predictors on the right-hand side.…”

Section: Autoregressive Integrated Moving Average (Arima) Modelsmentioning

confidence: 99%

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Trend Analysis and Forecasting of Long-Term Temperature Data to Anticipate Local Warming: A Case Study of Jawa Timur, Indonesia

Rohman

Wiyono

Fitriani

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Numerous studies have confirmed the significant rise in the mean land surface air temperature in recent decades at the global scale. However, studies on the land surface air temperature at local scales in Indonesia in recent decades are rare. Such studies are necessary to comprehend the imminence of the threat of climate change in regions with intensive agricultural industry, such as Jawa Timur, Indonesia. The first objective of this study is to analyse the trend of air temperature in six locations in Jawa Timur based on at least 30-year air temperature data retrieved from NOAA Global Summary of the Day data. After the significance or insignificance of the temperature trend is verified, the second objective is to compare the accuracies of two forecasting methods, namely Exponential Smoothing and ARIMA, on the long-term air temperature data in each location. The Mann-Kendall test along with sieve-bootstrapping is used in the air temperature trend analysis. Mean Absolute Error (MAE) metric is used to measure the accuracy of the methods. The trend analysis shows that statistically significant monotonic rises in air temperature are found in four locations, namely Bawean, Kalianget Madura, Surabaya Perak, and Banyuwangi. It is also found that ARIMA (3,1,3) is able to forecast the temperature data for 2021 with MAE < 1.30 degrees Celsius and it outperforms the Exponential Smoothing method.

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Section: Analysis Of Forecasting Modelsmentioning

confidence: 99%

Section: Autoregressive Integrated Moving Average (Arima) Modelsmentioning

confidence: 99%

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Trend Analysis and Forecasting of Long-Term Temperature Data to Anticipate Local Warming: A Case Study of Jawa Timur, Indonesia

Rohman

Wiyono

Fitriani

2022

IOP Conf. Ser.: Earth Environ. Sci.

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“…Metode ARIMA pada umumnya memberikan output yang lebih baik dibandingkan dengan metode-metode peramalan yang lainnya dan metode ini didasarkan pada model regresi deret waktu stasioner [7] [8]. Selain itu, metode ini memiliki kelebihan, yaitu memiliki tingkat akurasi yang cukup tinggi dalam melakukan peramalan, mempunyai sifat fleksibel (mengikuti pola data), dan metode ini dapat digunakan untuk memprediksi permintaan di masa yang akan datang dengan cepat, akurat, dan murah [9].…”

Section: Pendahuluanunclassified

Penerapan Model ARIMA Dalam Memprediksi Penjualan Produk Minuman Teh Botol Sosro Ukuran 350 mL

2022

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This study aims to provide suggestions for improvements in overcoming stock shortages of soft drink products using a forecasting method. The results of such forecasting will be compared with the forecasting methods used by the company at this time. The Autoregressive Integrated Moving Average (ARIMA) method was used in this study to improve the accuracy of demand forecasting in soft drink products (TBE 350 mL K12 Aseptic). This study used product sales data for the period January 2016 to January 2022. Based on the results of calculation and data processing, it is known that the best model is ARIMA (2,1,0) with a MAPE value of 35,966%. Meanwhile, the method used by the company has a MAPE value of 36.569%. It Shows that the ARIMA method (2,1,0) has better forecasting accuracy compared to the company's forecasting method with a MAPE difference of 0.604%. The validation results were obtained forecasting in January 2022 with ARIMA (2,1,0) of 22,569 cartons, while the company's method was 21,194 cartons. This shows that the ARIMA method (2,1,0) is more accurate in forecasting because it has a forecast value in the January 2022 period close to the actual demand value, which is 23,193 cartons. The ARIMA model equation (2,1,0) for forecasting soft drink products in the following month is Zt = 0,494Zt-1 + 0,210Zt-2 + 0,297Zt-3

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“…Depending on the time frame, forecasting can be categorized into three types: short-term (less than 3 months -1 year), medium-term (less than 3 years), and long-term (more than 3 years) [3]. Time series data can be used to forecast using several models, including Exponential Smoothing, Moving Average, and ARIMA [4]. The Exponential Smoothing method is more suitable for forecasting stationary data, while the Moving Average model is more accurate when used for data that does not have seasonal and trend elements.…”

Section: Introductionmentioning

confidence: 99%

Sales Prediction on the Diamond Cell Counter Using Autoregresive Integrated Moving Average (ARIMA) Method

Rahayu

Prasetyaningrum

2023

Journal-ISI

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Diamond Cell is a specialized retailer that offers a diverse range of smartphone accessories, electronic credits, and internet vouchers from different providers, each with varying active periods. However, the uncertainty surrounding internet voucher sales transactions often leaves counter owners hesitant to increase their stock due to the short active period of the vouchers. This leads to frequent customer dissatisfaction as the internet vouchers run out, resulting in lost sales opportunities. To address this issue, this study aimed to predict voucher sales for the upcoming month to serve as a reference for calculating the stock of voucher supply. The Auto-regressive Integrated Moving Average (ARIMA) method was used based on voucher sales data from November 2022 to January 2023. Out of the three tentative models obtained, only one proved suitable for use. The best ARIMA model was the (2,1,0) model, with a MAD value of 29.65, an MSE value of 2409.95, and a MAPE value of 23.3%. Based on the February voucher sales, the stock level can remain the same as the previous period since the sales were stable. The findings of this study can help Diamond Cell counter owners make more informed decisions about stocking internet vouchers, resulting in better customer satisfaction and reduced likelihood of losses.

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Analisis Prediksi Debit Sungai Amprong Dengan Model Arima (Autoregressive Integrated Moving Average) Sebagai Dasar Penyusunan Pola Tata Tanam

Cited by 9 publications

References 0 publications

Trend Analysis and Forecasting of Long-Term Temperature Data to Anticipate Local Warming: A Case Study of Jawa Timur, Indonesia

Trend Analysis and Forecasting of Long-Term Temperature Data to Anticipate Local Warming: A Case Study of Jawa Timur, Indonesia

Penerapan Model ARIMA Dalam Memprediksi Penjualan Produk Minuman Teh Botol Sosro Ukuran 350 mL

Sales Prediction on the Diamond Cell Counter Using Autoregresive Integrated Moving Average (ARIMA) Method

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