This study focuses on the novel forecasting method (SutteARIMA) and its application in predicting Infant Mortality Rate data Indonesia. It undertakes a comparison of most popular widely used four methods: ARIMA, Neural Networks Time Series (NNAR), Holt-Winters, SutteARIMA. The were obtained from website World Bank. consisted annual infant mortality rate (per 1000 live births) 1991 to 2019. To d...

Software is moving towards evolutionary architectures that are able to easily accommodate changes and integrate new functionality. This is important in a wide range of applications, from plugin-based end user applications to critical applications with high availability requirements. This work presents a component based framework that allows introducing adaptability to the distributed component-...

Bitcoin as the current leader in cryptocurrencies is a new asset class receiving significant attention in the financial and investment community and presents an interesting time series prediction problem. In this paper, some forecasting models based on classical like ARIMA and machine learning approaches including Kriging, Artificial Neural Network (ANN), Bayesian method, Support Vector Machine...

This research was made in order to see which method performance is better between the KNN (K-Nearest Neighbor) regression and multiple linear on Boston Housing data. The performace referred here MAE, RMSE, MAPE, R2. a predict something based closest training examples of an object. Meanwhile, forecasting technique involving more than one independent variable. comparison two methods results Mean ...

Forecasting involves all areas in predicting future events. Many problems can be solved by using a forecasting approach to become study the field of data science. that learns through light age is able solve with large-scale or big data. With data, performance k-Nearest Neighbor (k-NN) method tested several accuracy measurements. Generally, measurement uses MAPE so it necessary conduct sensitivi...

Day-Ahead Market offers electricity market participants the opportunity to trade one day ahead of real-time. For each hour, a separate Clearing Price is created in Market. This study aims predict hourly using deep learning techniques. In this context, 24-hour Prices were forecasted with MLP, CNN, LSTM, and GRU. LSTM had best average forecasting performance an 8.15 MAPE value, according results ...