scholarly journals PREDICTING THE BITCOIN PRICE USING LINEAR REGRESSION OPTIMIZED WITH EXPONENTIAL SMOOTHING

2021 ◽  
Vol 3 (3) ◽  
pp. 277-282
Author(s):  
Indah Suryani ◽  
Hani Harafani
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Time Series Data ◽  
Exponential Smoothing ◽  
Series Data ◽  
Linear Regression Method ◽  
Economic Uncertainty ◽  
Transformation Technique ◽  
Method Optimization ◽  
Exponential Smoothing Method

Bitcoin is one of the most popular cryptocurrencies today. In the current pandemic conditions that hit the world due to Covid-19, bitcoin is expected to be used as an investment when the level of economic uncertainty is high. In this study, the data used is bitcoin price data which is included in time series data. One of the commonly used methods for prediction in time series is the linear regression method. And to be able to develop the prediction results, a data transformation technique is used using the popular method, namely exponential smoothing. In the exponential smoothing method, optimization of the alpha parameter is carried out to be able to boost the prediction results from linear regression. And from the experimental results, it is evident that the optimization of the alpha parameter in exponential smoothing is able to improve the prediction performance of linear regression with the results of the comparison of RMSE with the t test which has resulted in significant differences.

scholarly journals Peramalan Penjualan Rebung Bambu Betung (Dendrocalamus asper) dengan Menggunakan Metode Triple Exponential Smoothing

2021 ◽  
Vol 9 (2) ◽  
pp. 177
Author(s):  
Ni Putu Murtini ◽  
I Gusti Ngurah Apriadi Aviantara ◽  
Ida Bagus Putu Gunadnya
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Series Data ◽  
Bamboo Shoots ◽  
Dendrocalamus Asper ◽  
Fresh Products ◽  
One Year ◽  
Exponential Smoothing Method

ABSTRAK Rebung bambu betung (Dendrocalamus asper) merupakan salah satu olahan produk segar yang dijual di Tiara Dewata Supermarket, dimana olahan tersebut terbagi menjadi tiga yaitu rebung mentah, rebung rajang, dan rebung biasa. Masa simpan rebung tergolong sangat singkat, hanya 1 – 3 hari. Lebih lanjut, penjualan yang terjadi setiap bulan untuk ketiga produk segar ini berfluktuasi dan sulit diduga kecenderungannya. Oleh karena itu, diperlukan metode peramalan agar dapat memperkecil kerugian yang akan terjadi. Tujuan penelitian ini adalah menemukan nilai alfa terbaik yang dapat digunakan untuk memperoleh data runtun waktu peramalan yang terbaik untuk periode satu tahun mendatang terhadap ketiga jenis olahan rebung bambu betung dengan metode Triple Exponential Smoothing. Data yang digunakan pada penelitian ini yaitu data aktual penjualan ketiga olahan rebung bambu betung dari bulan Maret 2019 – Mei 2020. Nilai alfa terbaik yang dapat digunakan untuk melakukan peramalan yaitu perhitungan data runtun waktu dengan nilai alfa 0,1 – 0,9 yang memiliki nilai kesalahan (error) terkecil, dimana alfa 0,3 pada rebung mentah dengan nilai kesalahan MSE 20,146, RSME 4,488, MAPE 19%, alfa 0,4 pada rebung rajang dengan nilai kesalahan MSE 120,281, RMSE 10,967, MAPE 5%, dan alfa 0,4 pada rebung biasa dengan nilai kesalahan MSE 1306,619, RMSE 36,147, MAPE 5%. Dari perhitungan menggunakan nilai alfa tersebut dapat disimpulkan bahwa metode triple exponential smoothing valid digunakan untuk meramalkan data runtun waktu penjualan ketiga olahan rebung bambu betung dari periode Juni 2020 – Mei 2021.  ABSTRACT Betung bamboo shoots (Dendrocalamus asper) is one of the processed fresh products sold at Tiara Dewata Supermarket, where the processing is divided into three, namely raw bamboo shoots, chopped bamboo shoots, and ordinary bamboo shoots. The shelf life of bamboo shoots is very short, only 1 - 3 days. Furthermore, the monthly sales for these three fresh products fluctuate and it is difficult to predict the trend. Therefore, a forecasting method is needed in order to minimize the losses that will occur. The purpose of this study was to find the best alpha value that can be used to obtain the best time series forecasting data for the next one year for the three types of Betung bamboo shoots using the Triple Exponential Smoothing method. The data used in this study is the actual sales data of the three processed bamboo bamboo shoots from March 2019 - May 2020. The best alpha value that can be used for forecasting is the calculation of time series data with an alpha value of 0.1 - 0.9 which has a value the smallest error, where alpha 0.3 in raw shoots with an error value of MSE 20.146, RSME 4.488, MAPE 19%, alpha 0.4 in chopped bamboo shoots with an error value of MSE 120.281, RMSE 10.967, MAPE 5%, and alpha 0,4 on ordinary shoots with an error value of MSE 1306,619, RMSE 36,147, MAPE 5%. From the calculation using the alpha value, it can be concluded that the triple exponential smoothing method is valid to predict the sales time series data of the three processed Betung bamboo shoots from the period June 2020 - May 2021.

scholarly journals FORECASTING SEASONAL TIME SERIES DATA USING THE HOLT-WINTERS EXPONENTIAL SMOOTHING METHOD OF ADDITIVE MODELS

2020 ◽  
Vol 16 (2) ◽  
pp. 151
Author(s):  
Nurhamidah Nurhamidah ◽  
Nusyirwan Nusyirwan ◽  
Ahmad Faisol
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Additive Model ◽  
Exponential Smoothing ◽  
Additive Models ◽  
Smoothing Method ◽  
Series Data ◽  
Seasonal Time Series ◽  
Exponential Smoothing Method ◽  
Forecasting Errors

The purpose of this study was to predict seasonal time series data using the Holt-Winters exponential smoothing additive model.  The data used in this study is data on the number of passengers departing at Hasanudin Airport in 2009-2019, the source of the data obtained from the official website of the Central Statistics Agency.  The results showed that the Holt-Winters exponential smoothing method on the passenger's number at Hasanudin Airport in 2009 to 2019 contained trend patterns and seasonal patterns, by first determining the initial values and smoothing parameters that could minimize forecasting errors.

scholarly journals MATHEMATICAL MODELLING AS AN ELEMENT OF PLANNING RAIL TRANSPORT STRATEGIES

Transport ◽  
2021 ◽  
Vol 36 (4) ◽  
pp. 354-363
Author(s):  
Anna Borucka ◽  
Dariusz Mazurkiewicz ◽  
Eliza Łagowska
Keyword(s):  
Time Series ◽  
Development Strategy ◽  
Time Series Data ◽  
Arima Model ◽  
Exponential Smoothing ◽  
Rail Transport ◽  
Transport Infrastructure ◽  
Rolling Stock ◽  
Series Data ◽  
Passenger Transport

Effective planning and optimization of rail transport operations depends on effective and reliable forecasting of demand. The results of transport performance forecasts usually differ from measured values because the mathematical models used are inadequate. In response to this applicative need, we report the results of a study whose goal was to develop, on the basis of historical data, an effective mathematical model of rail passenger transport performance that would allow to make reliable forecasts of future demand for this service. Several models dedicated to this type of empirical data were proposed and selection criteria were established. The models used in the study are: the seasonal naive model, the Exponential Smoothing (ETS) model, the exponential smoothing state space model with Box–Cox transformation, ARMA errors, trigonometric trend and seasonal components (TBATS) model, and the AutoRegressive Integrated Moving Average (ARIMA) model. The proposed time series identification and forecasting methods are dedicated to the processing of time series data with trend and seasonality. Then, the best model was identified and its accuracy and effectiveness were assessed. It was noticed that investigated time series is characterized by strong seasonality and an upward trend. This information is important for planning a development strategy for rail passenger transport, because it shows that additional investments and engagement in the development of both transport infrastructure and superstructure are required to meet the existing demand. Finally, a forecast of transport performance in sequential periods of time was presented. Such forecast may significantly improve the system of scheduling train journeys and determining the level of demand for rolling stock depending on the season and the annual rise in passenger numbers, increasing the effectiveness of management of rail transport.

scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI IMPOR BERAS DI INDONESIA

2019 ◽  
Vol 11 (2) ◽  
pp. 183-201
Author(s):  
Yona Namira ◽  
Iskandar Andi Nuhung ◽  
Mudatsir Najamuddin
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Series Data ◽  
Test Results ◽  
The Us ◽  
Rice Prices ◽  
Us Dollar ◽  
Spss Software

This study aims to 1) identify factors that affect the import of rice in Indonesia 2) analyze the influence of these factors on imports of rice in Indonesia. The data used in this research are time series data from 1994 to 2013 from the Central Statistics Agency (BPS), the Ministry of Agriculture, Ministry of Commerce, National Logistics Agency (Bulog), and Bank Indonesia. Multiple linear regression through SPSS software version 21 was employed to analyze the data. The test results together indicated the variables of productions, consumptions, stocks of rice, domestic rice prices, international rice prices and the rupiah against the US dollar affect the imports of rice in Indonesia.

scholarly journals Pengaruh Industri Sedang dan Besar Terhadap Tingkat Kemiskinan di Kabupaten Gresik Tahun 2002-2016

2021 ◽  
Vol 4 (1) ◽  
pp. 25-31
Author(s):  
Rohmatul Janah ◽  
Ida Nuraini
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Industrial Sector ◽  
Poverty Rate ◽  
Domestic Product ◽  
Series Data ◽  
Insignificant Effect ◽  
Poverty Levels

This research is aimed at studying the influence of medium and large industries on poverty levels in Gresik on 2002-2016. The variables used in this study is medium and large industries, a labour of medium and large industries, gross regional domestic product (GRDP) of industrial sector and poverty rate. The method used in this study used multiple linear regression and used time-series data. The results of this study simultaneously are the variables of the amount of medium and large industries, the labour medium and large industries, and the gross regional domestic product (GRDP) of the industrial sector to poverty rate is significant. While medium and large industries to poverty rate have negative and insignificant effect with a coefficient value of -0,208905. The labour of medium and large industries to poverty rate has a positive and significant effect with a coefficient value of 0,130822,  the gross regional domestic product (GRDP) of industrial to poverty rate has a negative and significant effect with a coefficient value of -0,169431.

Pengaruh pertumbuhan ekonomi, inflasi dan pengangguran terhadap tingkat kemiskinan di Sumatera Selatan

2019 ◽  
Vol 16 (1) ◽  
pp. 1-10
Author(s):  
Novegya Ratih Primandari
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Time Series Data ◽  
Secondary Data ◽  
Poverty Rate ◽  
Least Square ◽  
Series Data ◽  
Ordinary Least Square

This research aims to analyze effect of economic growth, inflation and Unemployment on the Rate of Poverty in the Province of South Sumatera. This research used secondary data in the form of time series data from 2001-2017. The method used quantitative approach by applying a linear regression model with OLS estimation Ordinary Least Square (OLS) method. The results of this study indicate that partially and simultaneously Economic Growth, Inflation and Unemployment have a significant effect on the Poverty Rate in the Province of South Sumatera.

scholarly journals Empirical Evaluation of Alternative Time-Series Models for COVID-19 Forecasting in Saudi Arabia

2021 ◽  
Vol 18 (16) ◽  
pp. 8660
Author(s):  
Isra Al-Turaiki ◽  
Fahad Almutlaq ◽  
Hend Alrasheed ◽  
Norah Alballa
Keyword(s):  
Time Series ◽  
Saudi Arabia ◽  
Time Series Data ◽  
Arima Model ◽  
Empirical Evaluation ◽  
Cubic Spline ◽  
Exponential Smoothing ◽  
Series Data ◽  
Time Series Models ◽  
Forecasting Models

COVID-19 is a disease-causing coronavirus strain that emerged in December 2019 that led to an ongoing global pandemic. The ability to anticipate the pandemic’s path is critical. This is important in order to determine how to combat and track its spread. COVID-19 data is an example of time-series data where several methods can be applied for forecasting. Although various time-series forecasting models are available, it is difficult to draw broad theoretical conclusions regarding their relative merits. This paper presents an empirical evaluation of several time-series models for forecasting COVID-19 cases, recoveries, and deaths in Saudi Arabia. In particular, seven forecasting models were trained using autoregressive integrated moving average, TBATS, exponential smoothing, cubic spline, simple exponential smoothing Holt, and HoltWinters. The models were built using publicly available daily data of COVID-19 during the period of 24 March 2020 to 5 April 2021 reported in Saudi Arabia. The experimental results indicate that the ARIMA model had a smaller prediction error in forecasting confirmed cases, which is consistent with results reported in the literature, while cubic spline showed better predictions for recoveries and deaths. As more data become available, a fluctuation in the forecasting-accuracy metrics was observed, possibly due to abrupt changes in the data.

scholarly journals PERAMALAN EKSPOR NONMIGAS DENGAN VARIASI KALENDER ISLAM MENGGUNAKAN X-13-ARIMA-SEATS (Studi Kasus: Ekspor Nonmigas Periode Januari 2013 sampai Desember 2017)

2018 ◽  
Vol 7 (3) ◽  
pp. 236-247
Author(s):  
Eka Lestari ◽  
Tatik Widiharih ◽  
Rita Rahmawati
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Foreign Exchange ◽  
Decomposition Method ◽  
Oil And Gas ◽  
Time Series Data ◽  
Series Data ◽  
Holiday Effect ◽  
Forecasting Method ◽  
Loading And Unloading

Non-oil and gas exports are one of the largest foreign exchange earners for Indonesia. Non-oil and gas exports always experience a decline in the month of Eid Al-Fitr due to delays in the delivery of export goods because the loading and unloading of goods at the port is reduced during Eid Al-Fitr. The shift of the Eid Al-Fitr month on the data will form a pattern or season with an unequal period called the moving holiday effect. The time series forecasting method that usually used the ARIMA method. Because the ARIMA method only suitable for time series data with the same seasonal period and can’t handle the moving holiday effect, the X-13-ARIMA-SEATS method used two steps. First, regARIMA modeling is a linear regression between time series data and the weight of Eid Al-Fitr and the residuals follow the ARIMA process. The weighting is based on three conditions, namely pre_holiday, post_holiday, and multiple. Second, X-12-ARIMA decomposition method for seasonal adjustments that produces trend-cycle components, seasonal, and irregular. Based on the analysis carried out on the monthly non-oil and gas export data for the period January 2013 to December 2017, the X-13-ARIMA-SEATS (1,1,0) model was obtained in the post_holiday condition as the best model. The forecasting results in 2018 show the largest decline in non-oil and gas exports in June 2018 which coincided with the Eid Al-Fitr holiday. MAPE value of 10.90% is obtained which shows that the forecasting ability is good.Keywords:  time series, non-oil and gas, X-13-ARIMA-SEATS, moving holiday

scholarly journals ANALISIS PENGARUH INFLASI, PRODUK DOMESTIK BRUTO (PDB), DAN NILAI TUKAR TERHADAP TABUNGAN MUDHARABAH PADA BANK UMUM SYARIAH (PERIODE 2013-2017)

2019 ◽  
Vol 8 (2) ◽  
pp. 138
Author(s):  
Rita Nur Wahyuningrum ◽  
Aan Zainul Anwar
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Linear Regression ◽  
Gross Domestic Product ◽  
Multiple Linear Regression ◽  
Time Series Data ◽  
Domestic Product ◽  
Series Data ◽  
Islamic Banking ◽  
Central Statistics

<p>This study aims to analyze the effect of inflation, gross domestic product (GDP) and rupiah exchange rate on Mudharabah savings in Islamic banking in Indonesia. The data used is time series data for the period March 2013 to September 2017, which was published by Bank Indonesia from the Islamic Banking Statistics Report and the Central Statistics Agency. The technique of analyzing the research is qualitative with the method of Multiple Linear Regression. The results of this study indicate that simultaneously the Inflation, Gross Domestic Product (GDP) and Exchange Rate variables together have a significant effect on Mudharabah Savings. While partially only the Exchange Rate variable has a significant effect on Mudharabah Savings. Inflation Variables and Gross Domestic Product (GDP) have no significant effect on Mudharabah Savings.</p><p> </p><p>Keyword: inflation, gross domestic product, exchange rate, mudharabah saving</p>

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