scholarly journals Comparison of Rice Price Forecasting Using the ARIMA Method on Amazon Forecast and Sagemaker

2020 ◽  
Vol 4 (3) ◽  
pp. 537-543
Author(s):  
Is Mardianto ◽  
Muhamad Ichsan Gunawan ◽  
Dedy Sugiarto ◽  
Abdul Rochman
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Price Forecasting ◽  
Price Prediction ◽  
Rice Price ◽  
Moving Average Processes ◽  
Auto Regressive ◽  
Independent Variable ◽  
Rice Prices ◽  
Using Data

Rice is one of the main commodities of trade in Indonesia. PT Food Station as the management company of Cipinang Rice Main Market every day publishes data on price, type of rice and the amount of rice that enters and exits Jakarta area. This study aims to forecast rice prices in the Jakarta area using data held by PT FoodStation during the 2016-2018 data period. Rice price prediction is carried out for the next 30 days using the Auto Regressive Integrated Moving Average (ARIMA) method on the Amazon Forecast and Amazon Sagemaker platforms. The ARIMA model is a form of regression analysis that measures the strength of one dependent variable that is relatively influential on other change variables. The ARIMA model is a special type of regression model in which the dependent variable is considered stationary and the independent variable is the lag or previous value of the dependent variable itself and the error lag. ARIMA is a combination of auto-regressive and moving average processes. The final result obtained in this experiment is that the ARIMA model on Amazon Sagemaker cloud computing is superior when compared to Amazon Forecast. From the experimental results obtained the results of Amazon Sagemaker RMSE (313.379941) are smaller than Amazon Forecast (322.4118029). So it can be concluded that the ARIMA model run at Amazon Sagemaker is more accurate than Amazon Forecast for forecasting the price of rice for 30 days at the Cipinang Rice Main Market

scholarly journals Forecasting of Paddy Grain and Rice's Price: An ARIMA (Autoregressive Integrated Moving Average) Model Application

2020 ◽  
Vol 14 (2) ◽  
pp. 224
Author(s):  
Fitri Ramadhani ◽  
Ketut Sukiyono ◽  
Melli Suryanty
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Future Price ◽  
International Level ◽  
Price Prediction ◽  
Rice Price ◽  
Stationary Test ◽  
Moving Average Model ◽  
Actual Price

A decision making for a long-term paddy grain and rice price guidelines need a future price prediction and a forecasting model that made based on time progression. The most popular model used is ARIMA. The common problem in forecasting the paddy grain and rice in Indonesia using this model was choosing the best model which fit all type of forecasting. This study aimed to determine the most appropriate ARIMA Model and forecast paddy grain and rice’s price on the farmer level, wholesale level, and international level. The prediction began after the stationary test and the best model selection conducted. The ARIMA model used was chosen by the lowest AIC and SC accuracy value. ARIMA Model used in this study were grain price on the farmer level (1,1,2), grain price on the milling level (1,1,2), rice price on the wholesale level (1,1,3), and rice price on the international level (3,1,7). The rice price prediction in the next sixth months on the farmer level was IDR 5,905.15/kg and the actual price was IDR 5,524.89/kg, on the milling level was IDR 6,014.35/kg and the actual price was IDR 5,641/kg, on the wholesale level was IDR 12,163.92/kg and the actual price IDR 12,115/kg, while the on the international level was US$ 462,065/Ton and the actual price was US$ 408/Ton. This study concluded that the price list at a different level of the market was requiring a different model of ARIMA.

scholarly journals Modeling and Forecasting Under-Five Mortality Rate in Nigeria using Auto-Regressive Integrated Moving Average Approach

2020 ◽  
pp. 347-360 ◽  
Author(s):  
Donalben Onome Eke ◽  
Friday Ewere
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Childhood Mortality ◽  
Live Births ◽  
Under Five ◽  
Child Deaths ◽  
Mortality Estimate ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
Using Data

Nigeria’s efforts aimed at reducing avoidable child deaths have been met with gradual and sustained progress. Despite the decline in childhood mortality in Nigeria in the last two decades, its prevalence still remain high in comparison to the global standard of mortality for children under the age of five which stands at 25 deaths per 1000 live births. Knowledge of the chances of Nigeria achieving this goal for childhood mortality will aid proper interventions needed to reduce the occurrence. Therefore, this paper employed the Auto-Regressive Integrated Moving Average (ARIMA) model for time series analysis to make forecast of under-five mortality in Nigeria up to 2030 using data obtained from the United Nation’s Inter Agency Group for Childhood Mortality Estimate (UN-IGME). The ARIMA (2, 1, 1) model predicted a reduction of up to 37.3% by 2030 at 95% confidence interval. Results from the study also showed that a reduction of over 300% in under-five mortality is required for Nigeria to be able to achieve the SDG goal for under-five mortality.

scholarly journals Maize Price Forecasting Using Auto Regressive Integrated Moving Average (ARIMA) Model

2017 ◽  
Vol 6 (8) ◽  
pp. 2887-2895
Author(s):  
Venkatesh Panasa ◽  
R. Vijaya Kumari ◽  
G. Ramakrishna ◽  
S. Kaviraju
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Price Forecasting ◽  
Auto Regressive

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

scholarly journals Growth, instability and forecast of marine products export from India

2016 ◽  
Vol 63 (4) ◽  
Author(s):  
Apu Das ◽  
Nalini Ranjan Kumar ◽  
Prathvi Rani
Keyword(s):  
Moving Average ◽  
Export Performance ◽  
Arima Model ◽  
South East Asia ◽  
Arima Models ◽  
Policy Makers ◽  
Increasing Trend ◽  
Marine Products ◽  
Auto Regressive ◽  
Instability Indices

This paper analysed growth and instability in export of marine products from India with an attempt to forecast the total export quantity of marine products from the country. The compound growth rates and instability indices of marine products export from India were estimated for major importing countries viz., Japan, USA, European Union, South-east Asia and Middle East; as more than 80% of the marine products export from India destines to these markets. The study revealed high compound growth rate and low instability in case of selected countries. The study also revealed that India’s marine products export concentrated mainly to those countries, which were falling in less desirable or least desirable category which has affected export performance of the country. Forecast of India’s marine products export was done by fitting univariate Auto Regressive Integrated Moving Average (ARIMA) models. ARIMA (1, 1, 0) was found suitable for modelling marine products export from India. The results of ARIMA model indicated increasing trend in export of Indian marine products. This calls for serious attention by policy makers to identify competitive and stable market destinations for marine products export which could help in harnessing the potential of marine products export from India.

Predicting Daily Confirmed COVID-19 Cases in India

2021 ◽  
pp. 34-51
Author(s):  
Sudip Singh
Keyword(s):  
Time Series ◽  
Root Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Family Welfare ◽  
Mean Square ◽  
Univariate Time Series ◽  
Partial Autocorrelation Function ◽  
Auto Regressive

India, with a population of over 1.38 billion, is facing high number of daily COVID-19 confirmed cases. In this chapter, the authors have applied ARIMA model (auto-regressive integrated moving average) to predict daily confirmed COVID-19 cases in India. Detailed univariate time series analysis was conducted on daily confirmed data from 19.03.2020 to 28.07.2020, and the predictions from the model were satisfactory with root mean square error (RSME) of 7,103. Data for this study was obtained from various reliable sources, including the Ministry of Health and Family Welfare (MoHFW) and http://covid19india.org/. The model identified was ARIMA(1,1,1) based on time series decomposition, autocorrelation function (ACF), and partial autocorrelation function (PACF).

scholarly journals Analysis and Forecast of Mining Fatalities in Cherat Coal Field, Pakistan

2021 ◽  
Vol 11 (4) ◽  
pp. 22-26
Author(s):  
Sajid Khan ◽  
Kausar Sultan Shah ◽  
Naeem Abbas ◽  
Abdur Rahman ◽  
Naseer Muhammad Khan
Keyword(s):  
Management System ◽  
Safety Management ◽  
Moving Average ◽  
Arima Model ◽  
Coal Field ◽  
Safety Management System ◽  
Term Forecast ◽  
Auto Regressive ◽  
Mineral Production

Mineral exploitation contributes to the economic growth of developing countries. Managing mineral production brought a more disturbing environment linked to workers' causalities due to scarcities in the safety management system. One of the barriers to attaining an adequate safety management system is the unavailability of future information relating to accidents causing fatalities. Policymakers always try to manage the safety system after each accident. Therefore, a precise forecast of the number of workers fatalities can provide significant observation to strengthen the safety management system. This study involves forecasting the number of mining workers fatalities in Cherat coal mines by using Auto-Regressive Integrating Moving Average Method (ARIMA) model. Workers' fatalities information was collected over the period of 1994 to 2018 from Mine Workers Federation, Inspectorate of Mines and Minerals and company records to evaluate the long-term forecast. Various diagnostic tests were used to obtain an optimistic model. The results show that ARIMA (0, 1, 2) was the most appropriate model for workers fatalities. Based on this model, casualties from 2019 to 2025 have been forecasted. The results suggest that policymakers should take systematic consideration by evaluating possible risks associated with an increased number of fatalities and develop a safe and effective working platform.

scholarly journals Toward automating post processing of aquatic sensor data

2021 ◽  
Author(s):  
Amber Jones ◽  
Tanner Jones ◽  
Jeffery Horsburgh
Keyword(s):  
Anomaly Detection ◽  
Moving Average ◽  
Control Process ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Time Series Regression ◽  
Quality Control Process ◽  
Auto Regressive ◽  
And Performance ◽  
Using Data

Sensors measuring environmental phenomena at high frequency commonly report anomalies related to fouling, sensor drift and calibration, and datalogging and transmission issues. Suitability of data for analyses and decision making often depends on manual review and adjustment of data. Machine learning techniques have potential to automate identification and correction of anomalies, streamlining the quality control process. We explored approaches for automating anomaly detection and correction of aquatic sensor data for implementation in a Python package (PyHydroQC). We applied both classical and deep learning time series regression models that estimate values, identify anomalies based on dynamic thresholds, and offer correction estimates. Techniques were developed and performance assessed using data reviewed, corrected, and labeled by technicians in an aquatic monitoring use case. Auto-Regressive Integrated Moving Average (ARIMA) consistently performed best, and aggregating results from multiple models improved detection. PyHydroQC includes custom functions and a workflow for anomaly detection and correction.

scholarly journals PERAMALAN LAJU INFLASI DENGAN METODE AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

2017 ◽  
Vol 14 (4) ◽  
pp. 524 ◽  
Author(s):  
Djawoto Djawoto
Keyword(s):  
Time Series ◽  
Price Index ◽  
Inflation Rate ◽  
Moving Average ◽  
Arima Model ◽  
Consumer Price Index ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Auto Regressive ◽  
Auto Regression

Auto Regression Integrated Moving Average (ARIMA) or the combination model of Auto Regression with moving average, is a linier model which is able to represent the stationary time series or non stationary time series. The purpose of this research is to forecast the inflation rate in November 2010 with the Consumer Price Index (CPI) by using ARIMA. The inflation indicator is very important to anticipate in making the Government’s policy and decision as well as for the citizen is for the information to determine what to do in related with savings and investment. By looking at the existing criteria, it is determined that the best model is ARIMA (1,1,0) or AR (1). Model ARIMA (1,1,0), the coefficient value AR (1) is significant,which has the most minimum value of Akaike Info Criterion (AIC) and Schwars Criterion (SC) compare toARIMA (0,1,1) or MA (1) and ARIMA (1,1,1) or AR (1) MA (1). In summarize, the ARIMA model used to forecast the valueof IHK is ARIMA (1,1,0).

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