Prediksi Data Time Series Harga Penutupan Saham Menggunakan Model Box Jenkins ARIMA

The ability to predict time series data on closing market prices is critical in determining a company's stock results. The development of an efficient stock market has a positive correlation with economic growth, in a country both in the short and long term. In practice, investors tend to invest in countries that have a stable economy, low crime. The rise and fall of stock prices has made many investors develop various effective strategies in predicting stock prices in the future with the aim of making investment decisions so that investors can guarantee their profits and minimize risk.As a result, the researchers developed a model that could accurately estimate precision. Time series data models are one of the most powerful methods to render assumptions in decisions containing uncertainty. The AutoRegressive Integrated Moving Average (ARIMA) model with the Box Jenskins time series procedure is one of the most commonly used prediction models for time series results. The steps for using the Box Jenskins ARIMA model for historical details of expected stock closing prices are outlined in this paper. BBYB and YELO stock data from yahoo.finance were used as historical data. The Aikake Information Criterion (AIC), Bayesian Information Criterion (BIC) / Schawrz Bayesia Criterion (SBC), Log Probability, and Root Mean Square Error (RMSE) are used to choose an effective model, and the model chosen is ARIMA (1 , 1,2). The findings suggest that the Jenkins ARIMA box model has a lot of scope for short-term forecasting, which may help investors make better decisions. Keywords: prediction, the stock's current closing price, Box Jenskins ARIMA mode

TRANSFER FUNCTION AND ARIMA MODEL FOR FORECASTING BI RATE IN INDONESIA

Fluctuating gold prices can have an impact on various sectors of the economy. Some of the impacts of rising and falling gold prices are inflation, currency exchange rates, and the value of the Bank Indonesia benchmark interest rate (BI Rate). The data was taken from the Indonesian Central Statistics Agency's official website (BPS) for the Bank Indonesia benchmark interest rate (BI Rate) value. Therefore, research on the value of the Bank Indonesia benchmark interest rate (BI Rate) is essential with the gold price as a control. The purpose of this study is to forecast the value of the Bank Indonesia reference interest rate (BI Rate) with a transfer function model where the input variable used is the price of gold and forecast the value of the Bank Indonesia benchmark interest rate (BI Rate) with the ARIMA model. The analysis results show that the best model for forecasting the Bank Indonesia reference interest rate (BI Rate) is a transfer function model with a value of , , , and a noise series model  with the MAPE value i

PREDICT URBAN AIR POLLUTION IN SURABAYA USING RECURRENT NEURAL NETWORK – LONG SHORT TERM MEMORY

Air is one of the primary needs of living things. If the condition of air is polluted, then the lives of humans and other living things will be disrupted. So it is needed to perform special handling to maintain air quality. One way to facilitate the prevention of air pollution is to make air pollutionforecasting by utilizing past data. Through the Environmental Office, the Surabaya City Government has monitored air quality in Surabaya every 30 minutes for various air quality parameters including CO, NO, NO2, NOx, PM10, SO2 and meteorological data such as wind direction, wind direction, wind speed, wind speed, global radiation, humidity, and air temperature. These data are very useful to build a prediction model for the forecast of air pollution in the future. With the large amount and variance of data generated from monitoring air quality in Surabaya city, a qualified algorithm is needed to process it. One algorithm that can be used is Recurrent Neural Network - Long Short Term Memory (RNN-LSTM). RNN-LSTM is built for sequential data processing such as time-series data. In this study, several analyses are performed. There are trend analysis, correlation analysis of pollutant values to meteorological data, and predictions of carbon monoxide pollutants using the Recurrent Neural Network - LSTM in the city of Surabaya correlated with meteorological data. The results of this study indicate that the best prediction model using RNN-LSTM with RMSE calculation gets an error of 1,880 with the number of hidden layer 2 and epoch 50 scenarios. The predicted results built can be used as a reference in determining the policy of the city government to deal with air pollution going forward

Forecasting Heterogeneous Patient Flow through Big Data Application in Medical Facilities for Rational Staffing

An approach to automating resource management of a service organization based on simulation modeling integrating the predicted input flows of patients, is considered. Methods for predicting input flows based on SARIMA, Holt-Winters, LSTM, and controlled recurrent GRU models have been investigated. The results of a computational experiment on predicting patient flows in a medical organization are presented. Based on the results, a meta-algorithm for forecasting the input flow and its further integration into the simulation model of the service process of a multidisciplinary healthcare organization was developed

PEMILIHAN METODE PERAMALAN UNTUK MENGURANGI BULLWHIP EFFECT PADA SISTEM RANTAI PASOK PRODUK SIDE VISOR DXXN (STUDI KASUS DI PERUSAHAAN PLASTIC INJECTION CIKARANG)

ABSTRAKPerusahaan Plastic Injection, Cikarang tempat dilakukannya penelitian ini adalah salah satu perusahaan yang bergerak di bidang injection molding dengan produk utamanya yaitu side visor untuk kendaraan roda empat. Dalam memenuhi permintaan customer seringkali terjadi variasi pada demand dan order yang diterima dari customer. Ketika terjadi variasi dalam permintaan pasar yang pada awalnya relatif stabil dengan persediaan di tingkat downstream berubah fluktuatif semakin besar hingga ke tahap upstream, variasi persediaan akan semakin membesar, sehingga membentuk pola seperti cambuk. Fenomena ini kemudian dinamakan bullwhip effect. Bullwhip effect tersebut menyebabkan tidak akuratnya keputusan dalam penentuan tingkat persediaan dan kapasitas produksi yang dibutuhkan, yang berdampak pada terganggunya aliran rantai pasok. Dengan tujuan untuk mengurangi bullwhip effect di level delivery, maka penulis menggunakan metode Autoregressive Integrated Moving Average (ARIMA) dan Single Exponential Smoothing (ES) untuk pemilihan metode peramalan yang tepat guna mengurangi bullwhip effect di level delivery. Hasil yang didapat menunjukan terjadi penurunan nilai bullwhip effect di level delivery sebesar 0,0171 poin

Study of Inflation using Stationary Test with Augmented Dickey Fuller & Phillips-Peron Unit Root Test (Case in Bukittinggi City Inflation for 2014-2019)

This classical regression model is designed to handle the relationship between stationary variables and should not be applied to non-stationary series. A time series data is said to be stationary if the mean, variance, and covariance remain constant over time. The problem associated with non-stationary variables, and often encountered by researchers when dealing with time series data, is spurious regression. A clear indicator of false regression is the low Durbin-Watson statistic but has a higher coefficient of determination (R2). Therefore, before doing modeling or forecasting using time series data, it is very important to do a stationary test. In this study, we use inflation data in the City of Bukittinggi from January 2014 to December 2019 as a case study. The data shows an uptrend and correlated error terms. Empirical results show that inflation data in Bukittinggi City is a stationary series