Application of the Naïve Bayes Algorithm and Simple Exponential Smoothing for Food Commodity Prices Forecasting

Lutfi, Muhammad; Muttaqien, Hidayatul; Apriliani, Aulia; Zainuddin, Hazriani; Yuyun, Yuyun

doi:10.4108/eai.2-5-2019.2284613

Cited by 5 publications

(3 citation statements)

References 7 publications

(8 reference statements)

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“…The authors (Dewi & Listiowarni, 2020;Şahinli, 2020) applied Holt-Winters (HW) to forecast various food prices. Some researchers (Lutfi et al, 2019;Fitria, 2018) utilized SES to predict food prices. Talwar and Goyal (2019) employed exponential smoothing techniques, e.g., SES, DES, and HW, to forecast coriander prices.…”

Section: Food Price Forecasting Using Several Approachesmentioning

confidence: 99%

An Extreme Learning Machine Approach for Forecasting the Wholesale Price Index of Food Products in India

Das,

Chakrabarti

2023

JST

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Precise food price forecasting is crucial for any country, and searching for appropriate approach(s) from an assortment of available strategies toward this objective is an open problem. The current Indian Wholesale Price Index (WPI) series contains sixty individual food items in the 'manufacture of food product' category. This work considered the monthly data from April 2011 to June 2022, i.e., one hundred thirty-five months' data of these sixty WPIs. The researchers extracted the linearity, curvature, and autocorrelation features for each WPI. The curvature and linearity-based grouping of these WPIs revealed that the WPIs are heterogeneous. This work proposed an extreme learning machine (ELM) approach for forecasting these WPIs. The present work employed the following twenty-two time-series forecasting techniques: six standard methods (Auto ARIMA, TSLM, SES, DES, TES, and Auto ETS), five neural networks (Auto FFNN, Auto GRNN, Auto MLP, Auto ELM, and proposed ELM), and eleven state-of-art techniques (two ARIMA-ETS based ensembles, an ARIMA-THETAF-TBATS based ensemble, one MLP, and seven LSTM-based models) to identify the best forecasting approach for these WPIs. For the majority of WPIs, the offered ELM attained suitable performance in the case of fifteen months of out-of-sample forecasting. Nearly eighty-seven percent of cases achieved high accuracy (MAPE ≤ ten) and outshined others. Upon accuracy comparison, both forecast-MAPE and forecast-RMSE, between the proposed ELM and others, this paper observed that the proposed ELM's performance is more favorable. This paper's findings imply that the proposed ELM is a promising prospect to offer accurate forecasts of these sixty WPIs.

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Section: Food Price Forecasting Using Several Approachesmentioning

confidence: 99%

An Extreme Learning Machine Approach for Forecasting the Wholesale Price Index of Food Products in India

Das,

Chakrabarti

2023

JST

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“…Such as forecasting for electricity load demand with alpha 0,9 [7]. Meanwhile, studied with alpha 0 to 1 have been carried out such as fish inventory prediction [8], rice price forecasting [9], liquefied petroleum gas [10] and prediction of monthly cargo weight [11] food commodity prices [12], forecasting Palm Oil real production [13] and wind energy predictions [14]. Furthermore, some researchers tried to integrate the Brown's Double Exponential Smoothing (B-DES) method with other methods such as predicting stock exchange composite index [15], and prices prediction [16] compared with Weighted Moving Average.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of the Smoothing Constant Values Among Exponential Smoothing Methods in Commodity Prices Forecasting

Hazriani

Yuyun²,

Razak³

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Commodity prices forecasting is one of the business functions to estimate future demand based on past data trend. This study aims to implement a trial and error technique of the constant (alpha α) value in the exponential smoothing method. Dealing with confusion that often researchers find in selecting an alpha (α) value among exponential smoothing families, which suits characteristics of the investigated case. As selection of the constant value precisely contributes to reduce the forecasting deviation. This paper used the alpha (α) value in the range 0,1 to 0,9 and utilized the mean absolute percentage error (MAPE) and Mean Absolute Error (MAE) as the parameter to know the grade of prediction. In data training, the authors used Single Exponential Smoothing (SES) and Brown’s Double Exponential Smoothing (B-DES) as methods to compare the results of prediction. It is addressed that forecasting with alpha (α) 0,1 is the most optimal values for Single Exponential Smoothing (SES) in this case with margin error 0,00036 of MAPE and 16,84 of MAE.

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“…Fungsi peramalan digunakan sebagai dasar bagi perancanaan kapasitas, anggaran, perencanaan penjualan, perencanaan produksi dan inventori, perencanaan sumber daya, serta perencanaan pembelian bahan baku (Heldi Diana, 2015). Hasil peramalan membantu memfasilitasi pemangku kepentingan yang terlibat dalam pengambilan keputusan terkait dengan stabilitas biaya dan ketersediaan stok (Lutfi et al, 2019). Dapat disimpulkan bahwa peramalan menjadi salah satu kebutuhan yang sangat penting bagi pihak manajemen restoran dalam menentukan strategi manajemen restoran, khususnya pada manajemen stok.…”

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Peramalan Tren Penjualan Menu Restoran Menggunakan Metode Single Moving Average

Apriliani

Zainuddin

Agussalim

et al. 2020

JTIIK

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Penelitian ini bertujuan untuk meramalkan tren penjualan menu pada restoran guna membantu pihak pengelola restoran dalam menentukan dan memberikan rekomendasi pengelolaan stok menu. Peramalan dilakukan dengan mengimplementasikan metode single moving average pada data transaksi penjualan selama periode 15 bulan, yakni bulan Januari-Desember 2018 dan Januari-Maret 2019 untuk menghasilkan ramalan bulanan dan harian. Total sampel data latih yang diolah sebanyak 10.515 record yang merupakan data transaksi penjualan pada bulan Januari-Desember tahun 2018, serta 2.246 record data bulan Januari-Maret 2019 sebagai data uji (untuk menguji akurasi ramalan). Hasil pengujian hasil ramalan bulanan untuk Top-10 menu menghasilkan perhitungan MAPE (Mean Absolut Percentage Error) sebesar 4% yang berarti tingkat akurasi sangat baik, yakni sebesar 96%. Sedangkan pengujian hasil ramalan harian menghasilkan MAPE yang cukup tinggi yaitu sebesar 39.2%, mengindikasikan nilai akurasi yang cukup rendah, yakni 60.8%. Meskipun akurasi untuk ramalan harian, masih rendah namun hasil penelitian ini dapat memberikan gambaran kepada pengelola hotel tentang rentang minimum-maksimal stok yang perlu disiapkan untuk menu tertentu pada hari-hari tertentu. Untuk memperoleh akurasi prediksi harian yang lebih akurat, penelitian ini akan dilanjutkan dengan mencoba metode lain serta menambah jumlah data latih. AbstractThis research aims to forecast sales trend of a restaurant menus to help the restaurant management in determaining and providing recommendations for managing stocks. Forecasting was performed by applying the single moving average towards fifteen months recorded data transaction, namely January to December 2018, and Januari to March 2019 to establish monthly and daily forecast. Total data training was 10.515 recods data transaction obtained from Januari to December 2018, while data testing was 2.246 record data transaction within Januari to March 2019. Result for montly forecast shows, that the average accuracy reached 96% (MAPE 4%) indicating the forecast is almost perfect. While, for daily forecast the average accuracy is only 60.8% (MAPE 39,2%) indicating that the forecast is less accurate. Although, accuracy of the daily forecast is considered less accurate, the result still can be used by the restaurant management to figure-out minimum and maximum amount of stock to be prepared for certain menus in certain days.

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Application of the Naïve Bayes Algorithm and Simple Exponential Smoothing for Food Commodity Prices Forecasting

Cited by 5 publications

References 7 publications

An Extreme Learning Machine Approach for Forecasting the Wholesale Price Index of Food Products in India

An Extreme Learning Machine Approach for Forecasting the Wholesale Price Index of Food Products in India

A Comparison of the Smoothing Constant Values Among Exponential Smoothing Methods in Commodity Prices Forecasting

Peramalan Tren Penjualan Menu Restoran Menggunakan Metode Single Moving Average

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