scholarly journals Forecasting Sugarcane Yield of India based on rough set combination approach

2021 ◽  
Vol 4 (2) ◽  
pp. 163-177
Author(s):  
Haresh Kumar Sharma ◽  
◽  
Kriti Kumari ◽  
Samarjit Kar ◽  
◽  
...  
Keyword(s):  
Rough Set ◽  
Moving Average ◽  
Weight Coefficient ◽  
Grey Model ◽  
Autoregressive Integrated Moving Average ◽  
Sugarcane Production ◽  
Double Exponential ◽  
Sugarcane Yield ◽  
Double Exponential Smoothing ◽  
Combination Approach

This study applied a novel rough set combination approach for forecasting sugarcane production in India. The paper uses autoregressive integrated moving average (ARIMA), double exponential smoothing (DES) and Grey model (GM) to generate the single forecasts. Also, the weight coefficient is evaluated by underlying the rough set approach to combine the single forecasts obtained from different models. To validate our proposed analysis, Sugarcane from 1950 to 2011 was used for the overall empirical analysis and generate out-sample forecasts from 2012 to 2021 for the comparative analysis. Also, ARIMA (2, 1, 1) model is found more appropriate for forecasting Sugarcane production.

scholarly journals PERBANDINGAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DAN METODE DOUBLE EXPONENTIAL SMOOTHING DARI HOLT DALAM MERAMALKAN NILAI IMPOR DI INDONESIA

2020 ◽  
Vol 1 (1) ◽  
pp. 37-46
Author(s):  
YULINAR I. AJUNU ◽  
NOVIANITA ACHMAD ◽  
MUHAMMAD REZKY FRIESTA PAYU
Keyword(s):  
Optimal Parameter ◽  
Moving Average ◽  
Model Identification ◽  
Accurate Method ◽  
Exponential Smoothing ◽  
Autoregressive Integrated Moving Average ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Value Determination ◽  
Data Plotting

As a form of purchased goods from other state’s imports have impacts both positive and negative to the states’s condition; therefore, prediction is required. Employing Autoregressive Integrated Moving Average (ARIMA) and Holt’s Double Exponential Smoothing (DES) methods, this study intends to identify which of the methods is the most accurate to predict Indonesia’s import value.  The ARIMA method stage involved: data ploting, data stasioneriation, temporary model identification, parameter estimation, test residual assumption, and prediction. Moreover, the Holt’s DES method involved: data plotting, initial value determination, optimal parameter identification, Level Lt and Trend Tt value quantification, andprediction. The result shows that ARIMA method is the most accurate method to predict Indonesia’s import value.

scholarly journals Forecasting daily confirmed COVID-19 cases in Algeria using ARIMA models

2020 ◽  
Author(s):  
Messis Abdelaziz ◽  
Adjebli Ahmed ◽  
Ayeche Riad ◽  
Ghidouche Abderrezak ◽  
Ait-Ali Djida
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Arima Models ◽  
Forecast Period ◽  
Autoregressive Integrated Moving Average ◽  
Hospital Reform ◽  
Estimated Parameters ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method

ABSTRACTCoronavirus disease has become a worldwide threat affecting almost every country in the world. The aim of this study is to identify the COVID-19 cases (positive, recovery and death) in Algeria using the Double Exponential Smoothing Method and an Autoregressive Integrated Moving Average (ARIMA) model for forecasting the COVID-19 cases.The data for this study were obtained from March 21st, 2020 to November 26th, 2020. The daily Algerian COVID-19 confirmed cases were sourced from The Ministry of Health, Population and Hospital Reform of Algeria. Based on the results of PACF, ACF, and estimated parameters of the ARIMA model in the COVID-19 case in Algeria following the ARIMA model (0,1,1). Observed cases during the forecast period were accurately predicted and were placed within the prediction intervals generated by the fitted model. This study shows that ARIMA models with optimally selected covariates are useful tools for monitoring and predicting trends of COVID-19 cases in Algeria.

scholarly journals USULAN PERMINTAAN PRODUK SN 5 ML DI PT. XYZ DENGAN METODE TIME SERIES

2021 ◽  
Vol 8 (2) ◽  
pp. 117-122
Author(s):  
Sambas Sundana ◽  
Destri Zahra Al Gufronny
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Single Exponential ◽  
Weighted Moving Average

Permasalahan yang dihadapi PT. XYZ yaitu kesulitan dalam menentukan jumlah permintaan produk yang harus tersedia untuk periode berikutnya agar tetap dapat memenuhi kebutuhan pelanggan dan tidak menyebabkan penumpukan barang dalam jangka waktu yang lama terutama produk SN 5 ML yang memiliki permintaan jumlah paling besar dari produk lainnya. Tujuan dari penelitian ini yaitu menentukan metode peramalan yang tepat untuk meramalkan jumlah permintaan produk SN 5 ml periode Januari sampai dengan Desember 2021 Metode yang digunakan dalam penelitian ini yaitu metode peramalan Moving Average (MA), Weighted Moving Average (WMA), Single Exponential Smoothing (SES), dan Double Exponential Smoothing (DES). Adapun langkah langkah peramalan yang dilakukan yaitu menentukan tujuan peramalan,memilih unsur apa yang akan diramal, menentukan horizon waktu peramalan (pendek, menengah, atau panjang), memilih tipe model peramalan, mengumpulkan data yang di perlukan untuk melakukan peramalan, memvalidasi dan menerapkan hasil peramalan Berdasarkan perhitungan didapat metode peramalan dengan persentase tingkat kesalahan terkecil dibandingkan dengan metode lainnya yaitu  metode Moving Average (MA) dengan hasil yang diperoleh permintaan produk SN 5 ML pada bulan Januari sampai dengan Desember 2021 yaitu sebanyak 22.844.583 unit

scholarly journals PREDIKSI KREDIT DAN INDEKS MUSIMAN (SEASONAL INDEX) PADA BANK PERKREDITAN RAKYAT DI KABUPATEN JEMBER

BISMA ◽  
2020 ◽  
Vol 14 (3) ◽  
pp. 210
Author(s):  
Hari Sukarno ◽  
Ratna Pratiwi Nugroho ◽  
Susanti Prasetiyaningtiyas
Keyword(s):  
Moving Average ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Development Pattern ◽  
Research Approach ◽  
Financial Reports ◽  
Double Exponential ◽  
Study Results ◽  
Double Exponential Smoothing ◽  
Seasonal Index

This research aims to analyze the credit's predictive value, the development pattern of credit distribution, and the credit fluctuations of 13 Rural Banks in Jember, influenced by seasonal index variables, credit interest, NPL, LDR, ROA, CAR, and operational efficiency ratio. This study used an explanatory research approach. The sample consisted of all Rural Banks' quarterly financial reports in 2014-2019 taken by a purposive sampling method. Data were analyzed using three methods, i.e., double exponential smoothing, moving average ratio, and multiple linear regression analysis methods. Results showed that, according to each data analysis method, ten Rural Banks experienced increased credit distribution. However, the other three Rural Banks experienced a decrease in credit distribution. The study results also indicated an increasing trend in the development pattern of credit distribution. Meanwhile, the NPL and LDR variables partially influenced credit fluctuations. Keywords: credit prediction, rural bank, seasonal index

scholarly journals ANALISIS PERAMALAN DAN PERMINTAAN KREDIT SEKTOR PERTANIAN PADA PT.BRI (PERSERO) UNIT KEPAHIANG I

2004 ◽  
Vol 3 (2) ◽  
pp. 82-91
Author(s):  
Redy Badrudin ◽  
Bambang Sumantri ◽  
Meiliza Cecilia
Keyword(s):  
Agricultural Sector ◽  
Moving Average ◽  
Secondary Data ◽  
Regression Function ◽  
Agricultural Credit ◽  
Factors Influencing ◽  
Double Exponential ◽  
Interest Level ◽  
Double Exponential Smoothing ◽  
Credit Data

The study  was aimed to Know   growth and forecast the amount of demand for agricultural sector credit at PT. BRI (Persero) Unit Kepahiang I, and (2) examine factors influencing for agricultural sector credit. Data  was secondary data from 1993  up to 2003. Ratio  to Moving Average Method, Double Exponential Smoothing with moved period 3 monthly and regression function of Non Doubled Linear were used. Results of research indicates that growth of demand for agricultural credit at PT. SRI (Persero) Unit Kepahiang I tend to fluctuate. Forecasting of demand for agricultural sector creditb to period quarterly at 2003 till 2004, tend   to have experience decreasing significantly compare to previous period. Overall of factors influencing demand of agricultural sector is rate of interest level (X1), price level (X2), and agriculture product exchange rate (X3), while earning level (X4) does not have an effect on demand Key Words: BRI, Forecasting, demand for credits,agricultura/s , sectors

scholarly journals Komparasi Metode Peramalan Jumlah Permintaan Kamar Hotel

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Rendra Gustriansyah ◽  
Wilza Nadia ◽  
Mitha Sofiana
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Standard Type ◽  
The Public ◽  
Forecasting Methods ◽  
Forecasting Method ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Single Exponential

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>Hotel is  a type of accommodation that uses most or all of the buildings to provide lodging, dining and drinking services, and other services for the public, which are managed commercially so that each hotel will strive to optimize its functions in order to obtain maximum profits. One such effort is to have the ability to forecast the number of requests for hotel rooms in the coming period. Therefore, this study aims to forecast the number of requests for hotel rooms in the future by using five forecasting methods, namely linear regression, single moving average, double moving average, single exponential smoothing, and double exponential smoothing, as well as to compare forecasting results with these five methods so that the best forecasting method is obtained. The data used in this study is data on the number of requests for standard type rooms from January to November in 2018, which were obtained from the Bestskip hotel in Palembang. The results showed that the single exponential smoothing method was the best forecasting method for data patterns as in this study because it produced the smallest MAPE value of 41.2%.</em></p><p><strong><em>Keywords</em></strong><em>: forecasting, linier regression, moving average, exponential smoothing.</em></p><p align="center"><strong><em>Abstrak</em></strong></p><p><em>Hotel merupakan jenis akomodasi yang mempergunakan sebagian besar atau seluruh bangunan untuk menyediakan jasa penginapan, makan dan minum serta jasa lainnya bagi umum, yang dikelola secara komersial, sehingga setiap hotel akan berupaya untuk mengoptimalkan fungsinya agar memperoleh keuntungan maksimum. Salah satu upaya tersebut adalah memiliki kemampuan untuk meramalkan jumlah permintaan terhadap kamar hotel pada periode mendatang. Oleh karena itu, penelitian ini bertujuan untuk meramalkan jumlah permintaan terhadap kamar hotel di  masa mendatang dengan menggunakan lima metode peramalan, yaitu regresi linier, single moving average, double moving average, single exponential smoothing, dan double exponential smoothing, serta untuk mengetahui perbandingan hasil peramalan dengan kelima metode tersebut sehingga diperoleh metode peramalan terbaik. Adapun data yang digunakan dalam penelitian ini merupakan data jumlah permintaan kamar tipe standar dari bulan Januari hingga November tahun 2018, yang diperoleh dari hotel Bestskip Palembang. Hasil penelitian menunjukkan bahwa metode single exponential smoothing merupakan metode peramalan terbaik untuk pola data seperti pada penelitian ini karena menghasilkan nilai MAPE paling kecil sebesar 41.2%.</em></p><strong><em>Kata kunci</em></strong><em>: peramalan, regeresi linier, moving average, exponential smoothing.</em>

scholarly journals Forecasting Sugarcane yield of India using ARIMA-ANN hybrid model

2018 ◽  
Vol 33 (01) ◽  
Author(s):  
Mrinmoy Ray ◽  
R. S. Tomar ◽  
Ramasubramanian V. ◽  
K. N. Singh
Keyword(s):  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Main Stream ◽  
Research Activity ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Cash Crops ◽  
Sugarcane Yield ◽  
The Individual

Sugarcane is one of the main cash crops of India hence forecasting sugarcane yield is vital for proper planning. Till date Autoregressive integrated moving average (ARIMA) model is a stand out amongst the most main stream approach for sugarcane yield forecasting. Recent research activity reveals that hybrid model improves the accuracy of forecasting when contrasted with the individual model. Along these lines, in this study, ARIMA-ANN hybrid model was utilized for forecasting sugarcane yield of India. The hybrid model was compared with ARIMA approach. Empirical results clearly reveal that the forecasting accuracy of the hybrid model is superior to ARIMA.

scholarly journals Wavelet Transform and Autoregressive Integrated Moving Average Combination Approach in Crude Oil Prices Forecasting

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Nurull Qurraisha Nadiyya Md-Khair ◽  
Ruhaidah Samsudin ◽  
Ani Shabri
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Crude Oil ◽  
Moving Average ◽  
Absolute Error ◽  
Time Series Forecasting ◽  
Autoregressive Integrated Moving Average ◽  
Spot Prices ◽  
Crude Oil Prices ◽  
Combination Approach

This paper proposes a time series forecasting approach combining wavelet transform and autoregressive integrated moving average (ARIMA) to enhance the precision in forecasting crude oil spot prices series. Wavelet transform splits the original prices series into several subseries, then the most appropriate model of ARIMA is established to predict each respective series and finally all series are combined back to get the original series. The datasets for the experiment consist of crude oil spot prices from Brent North Sea (Brent) and West Texas Intermediate (WTI). Single forecasting model ARIMA and several existing forecasting approaches in the literatures are used to measure the performance of the proposed approach by utilizing the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) collected. Final results have depicted that the proposed approach outperforms other approaches with smaller MAE and RMSE values. Ultimately, it is proven that data decomposition, combined with forecasting method can increase the accuracy in time series forecasting.

scholarly journals PREDIKSI INFLASI DI INDONESIA MENGGUNAKAN METODE MOVING AVERAGE, SINGLE EXPONENTIAL SMOOTHING DAN DOUBLE EXPONENTIAL SMOOTHING

2020 ◽  
Vol 1 (2) ◽  
pp. 123-129
Author(s):  
Nugroho Arif Sudibyo ◽  
Ardymulya Iswardani ◽  
Arif Wicaksono Septyanto ◽  
Tyan Ganang Wicaksono
Keyword(s):  
Moving Average ◽  
Exponential Smoothing ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Single Exponential

Tujuan dari penelitian ini adalah untuk mengetahui model peramalan yang paling baik digunakan untuk meramalkan inflasi di Indonesia dengan data inflasi Januari 2015 sampai dengan Mei 2020. Penelitian ini menggunakan beberapa metode peramalan. Berdasarkan metode peramalan yang dilakukan didapatkan hasil peramalan yang paling baik dilihat dari MAPE, MAD dan MSD adalah single exponential smoothing. Selanjutnya, hasil peramalan menunjukkan bahwa tingkat inflasi di Indonesia pada Agustus 2020 sebesar  1,41746%.

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