Feed Forward Neural Networks for Forecasting Indonesia Exchange Composite Index

Herliansyah, Riki; Jamilatuzzahro, Jamilatuzzahro

doi:10.5176/2251-3388-4.1.77

Cited by 7 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That's also feasible by believing that the model encompasses a wide variety of distributions. We are leaving future studies to combine h-likelihood in the deep learning [39,40,[55][56][57][58][59], and using this framework towards spatial and remote sensing [60][61][62][63][64], hybrid forecasting [65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80], and more advanced disease detection cases using image detection [81][82][83][84][85][86][87][88][89][90]. = 0 .…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Albatross analytics a hands-on into practice: statistical and data science application

et al. 2022

View full text Add to dashboard Cite

Albatross Analytics is a statistical and data science data processing platform that researchers can use in disciplines of various fields. Albatross Analytics makes it easy to implement fundamental analysis for various regressions with random model effects, including Hierarchical Generalized Linear Models (HGLMs), Double Hierarchical Generalized Linear Models (DHGLMs), Multivariate Double Hierarchical Generalized Linear Models (MDHGLMs), Survival Analysis, Frailty Models, Support Vector Machines (SVMs), and Hierarchical Likelihood Structural Equation Models (HSEMs). We provide 94 types of dataset examples.

show abstract

Section: Conclusion and Future Researchmentioning

confidence: 99%

Albatross analytics a hands-on into practice: statistical and data science application

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The MSE and the MAE are both measures of accuracy and the degree of spread of data points [7]. The MAE is a measurement of how close forecasts are to the actual data points, the average of the absolute errors [18]. Values predicted from the training sample and values in the test set were utilised to calculate the performance measures.…”

Section: Neural Network Model Selectionmentioning

confidence: 99%

“…Over the years, there has been a substantial use and comparison between neural networks and traditional time series forecasting techniques in different areas of applications, such as health [15], geophysics [16], geomechanics [17], stock markets [18], chemical engineering [19,20], electrical engineering [21], global logistics [22], construction engineering [23], financial business support [24], and in insurance [25]. [13] made a comparison between neural networks and traditional forecasting methods in inventory management.…”

Section: Introductionmentioning

confidence: 99%

“…A few other researchers such as [27,28] have made an attempt at applying neural networks on univariate data and made comparisons with the traditional methods. [18], in their research study on the application of feed-forward neural networks (FFNN) for forecasting Indonesia exchange composite index, concluded that with less data observations (54 training data points, 13 model validation data points), FFNN was still able to produce accurate predictions, react to sensitive changes, and model the trend series well. Such comparisons have not been applied to a case of demand for public emergency ambulance services.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Univariate Time Series Analysis of Short-Term Forecasting Horizons Using Artificial Neural Networks: The Case of Public Ambulance Emergency Preparedness

Mapuwei

Bodhlyera

Mwambi

2020

Journal of Applied Mathematics

View full text Add to dashboard Cite

This study examined the applicability of artificial neural network models in modelling univariate time series ambulance demand for short-term forecasting horizons in Zimbabwe. Bulawayo City Councils’ ambulance services department was used as a case study. Two models, feed-forward neural network (FFNN) and seasonal autoregressive integrated moving average, (SARIMA) were developed using monthly historical data from 2010 to 2017 and compared against observed data for 2018. The mean absolute error (MAE), root mean square error (RMSE), and paired sample t-test were used as performance measures. Calculated performance measures for FFNN were MAE (94.0), RMSE (137.19), and the test statistic value p=0.493(>0.05) whilst corresponding values for SARIMA were 105.71, 125.28, and p=0.005(<0.05), respectively. Findings of this study suggest that the FFNN model is inclined to value estimation whilst the SARIMA model is directional with a linear pattern over time. Based on the performance measures, the parsimonious FFNN model was selected to predict short-term annual ambulance demand. Demand forecasts with FFNN for 2019 reflected the expected general trends in Bulawayo. The forecasts indicate high demand during the months of January, March, September, and December. Key ambulance logistic activities such as vehicle servicing, replenishment of essential equipment and drugs, staff training, leave days scheduling, and mock drills need to be planned for April, June, and July when low demand is anticipated. This deliberate planning strategy would avoid a dire situation whereby ambulances are available but without adequate staff, essential drugs, and equipment to respond to public emergency calls.

show abstract

Comparison study of crude oil price forecasting using generalized regression neural network and feed forward neural network

Kariyam

Yuwinda

2018

AIP Conference Proceedings

View full text Add to dashboard Cite

Feed Forward Neural Networks for Forecasting Indonesia Exchange Composite Index

Cited by 7 publications

References 0 publications

Albatross analytics a hands-on into practice: statistical and data science application

Albatross analytics a hands-on into practice: statistical and data science application

Univariate Time Series Analysis of Short-Term Forecasting Horizons Using Artificial Neural Networks: The Case of Public Ambulance Emergency Preparedness

Comparison study of crude oil price forecasting using generalized regression neural network and feed forward neural network

Contact Info

Product

Resources

About