scholarly journals Power Business Intelligence in the Data Science Visualization Process to Forecast CPO Prices

2021 ◽  
Vol 2 (6) ◽  
pp. 2198-2208
Author(s):  
Albara ◽  
Al-Khowarizmi ◽  
Riyan Pradesyah
Keyword(s):  
Time Series ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Data Science ◽  
Crude Palm Oil ◽  
Business Process Intelligence ◽  
Visualization Process ◽  
Time Series Method ◽  
Artificial Neural Network Ann ◽  
Data Visualizations

Forecasting is one of the techniques in data mining by utilizing the data available in the data warehouse. With the development of science, forecasting techniques have also entered the computational field where the forecasting technique uses the artificial neural network (ANN) method. Where is the method for simple forecasting using the Time Series method. However, the ability to create data visualizations certainly hinders researchers from maximizing research results. Of course, with the development of the Power BI software, the data science process is more neatly presented in the form of visualization, where the data science process involves various fields so that in this paper the results of forecasting the price of crude palm oil (CPO) are presented for the development of the CPO business with the hope of implementing the Business Process. intelligence (BI) by involving ANN, namely the time series for forecasting. From the final results, accuracy in forecasting with time series involves 2 accuracy techniques, the first using MAPE and getting a result of 0.03214% and the second using MSE to get 962.91 results.

scholarly journals The effect of a SECoS in crude palm oil forecasting to improve business intelligence

2020 ◽  
Vol 9 (4) ◽  
pp. 1604-1611 ◽  
Author(s):  
Al-Khowarizmi Al-Khowarizmi ◽  
Ilham Ramadhan Nasution ◽  
Muharman Lubis ◽  
Arif Ridho Lubis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transaction Costs ◽  
Palm Oil ◽  
Business Intelligence ◽  
Business Processes ◽  
Crude Palm Oil ◽  
The Future ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Crude palm oil is a crop that has a harvest period of ± 2 weeks and is in dire need of dissemination of information using e-commerce in order to be able to predict the price of the yield of companies or individual gardens within the next 2 weeks in order to improve studies on business intelligence. The disadvantage of not implementing e-commerce is certainly detrimental to the garden owner because they have to go through an agent so prices are set based on the agent. So with the application of e-commerce, buyers of crude palm oil can predict prices in conducting business processes to the future. So the need to forecasting the price of crude palm oil heads in order to improve the application of business intelligence using the evolution-based artificial neural network (ANN) method which in this paper is tested with SECoS get a MAPE value of 0.035% and by applying business intelligence can protect transaction costs by 33.3%.

scholarly journals Forecasting Crude Palm Oil Prices Using Fuzzy Rule-Based Time Series Method

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 32216-32224 ◽  
Author(s):  
Nur Fazliana Rahim ◽  
Mahmod Othman ◽  
Rajalingam Sokkalingam ◽  
Evizal Abdul Kadir
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Fuzzy Rule ◽  
Oil Prices ◽  
Series Method ◽  
Crude Palm Oil ◽  
Rule Based ◽  
Time Series Method

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

2019 ◽  
Vol 13 ◽  
Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur
Keyword(s):  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
End Users ◽  
Processing Stage ◽  
Information Update ◽  
On Line ◽  
Analytical Processing ◽  
Warehouse Operations ◽  
Made In

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Method: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

The Data Science of COVID-19 Spread: Some Troubling Current and Future Trends

2020 ◽  
Vol 26 (3) ◽  
Author(s):  
Rex W. Douglass ◽  
Thomas Leo Scherer ◽  
Erik Gartzke
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Observational Studies ◽  
Data Science ◽  
Future Trends ◽  
Methodological Issues

AbstractOne of the main ways we try to understand the COVID-19 pandemic is through time series cross section counts of cases and deaths. Observational studies based on these kinds of data have concrete and well known methodological issues that suggest significant caution for both consumers and produces of COVID-19 knowledge. We briefly enumerate some of these issues in the areas of measurement, inference, and interpretation.

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