Assessment of autoregressive integrated moving average (ARIMA), generalized linear autoregressive moving average (GLARMA), and random forest (RF) time series regression models for predicting influenza A virus frequency in swine in Ontario, Canada

This study aims to determine the impact of COVID-19 cases in Indonesia on the USD/IDR exchange rate using the Transfer Function Model and Vector Autoregressive Moving-Average with Exogenous Regressors (VARMAX) Model. This paper uses daily data on the COVID-19 case in Indonesia, the USD/IDR exchange rate, and the IDX Composite period from 1 March to 29 June 2020. The analysis shows: (1) the higher the increase of the number of COVID-19 cases in Indonesia will significantly weaken the USD/IDR exchange rate, (2) an increase of 1% in the number of COVID-19 cases in Indonesia six days ago will weaken the USD/IDR exchange rate by 0.003%, (3) an increase of 1% in the number of COVID-19 cases in Indonesia seven days ago will weaken the USD/IDR exchange rate by 0.17%, and (4) an increase of 1% in the number of COVID-19 cases in Indonesia eight days ago will weaken the USD/IDR exchange rate by 0.24%.

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Peramalan Banyak Pengunjung Pantai Pandasimo Bantul Menggunakan Regresi Runtun Waktu dan Seasonal Autoregressive Integrated Moving Average Exogenous

Indonesian Journal of Applied Statistics ◽

10.13057/ijas.v4i1.45795 ◽

2021 ◽

Vol 4 (1) ◽

pp. 57

Author(s):

Tito Tatag Prakoso ◽

Etik Zukhronah ◽

Hasih Pratiwi

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

Seasonal Pattern ◽

Series Data ◽

Gregorian Calendar ◽

Time Series Regression ◽

Autoregressive Integrated Moving Average ◽

Past Data

<p>Forecasting is a ways to predict what will happen in the future based on the data in the past. Data on the number of visitors in Pandansimo beach are time series data. The pattern of the number of visitors in Pandansimo beach is influenced by holidays, so it looks like having a seasonal pattern. The majority of Indonesian citizens are Muslim who celebrate Eid Al-Fitr in every year. The determination of Eid Al-Fitr does not follow the Gregorian calendar, but based on the Lunar calendar. The variation of the calendar is about the determination of Eid Al-Fitr which usually changed in the Gregorian calendar, because in the Gregorian calendar, Eid Al-Fitr day will advance one month in every three years. Data that contain seasonal and calendar variations can be analyzed using time series regression and Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX) models. The aims of this study are to obtain a better model between time series regression and SARIMAX and to forecast the number of Pandansimo beach visitors using a better model. The result of this study indicates that the time series regression model is a better model. The forecasting from January to December 2018 in succession are 13255, 6674, 8643, 7639, 13255, 8713, 22635, 13255, 13255, 9590, 8549, 13255 visitors.</p><strong>Keywords: </strong>time series regression, seasonal, calendar variations, SARIMAX, forecasting

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Forecasting Stock Price PT. Telkom Using Hybrid Time Series Regression Linear– Autoregressive Integrated Moving Average Model

Jurnal Matematika Statistika dan Komputasi ◽

10.20956/j.v18i2.18837 ◽

2022 ◽

Vol 18 (2) ◽

pp. 293-307

Author(s):

Kartika Ramadani ◽

Sri Wahyuningsih ◽

Memi Nor Hayati

Keyword(s):

Time Series ◽

Linear Model ◽

Stock Price ◽

Moving Average ◽

Arima Model ◽

Residual Value ◽

Time Series Regression ◽

Autoregressive Integrated Moving Average ◽

Trend Pattern ◽

Closing Price

The hybrid method is a method of combining two forecasting models. Hybrid method is used to improve forecasting accuracy. In this study, the Time Series Regression (TSR) linear model will be combined with the Autoregressive Integrated Moving Average (ARIMA) model. The TSR linear model is used to obtain the model and residual value, then the residual value of the TSR linear model will be modeled by the ARIMA model. This combination method will produce a hybrid TSR linear-ARIMA model. The case study in this research is stock closing price (daily) of PT. Telkom Indonesia Tbk. The stock closing price (daily) of PT. Telkom Indonesia Tbk in 2020 showed an decreasing and increasing trend pattern. The results of this study obtained the best model of hybrid TSR linear-ARIMA (2,1,1) with the proportion of data training and testing is 70:30. In the best model, the MAD value is 56.595, the MAPE value is 1.880%, and the RMSE value is 78.663. It is also found that the hybrid TSR linear-ARIMA model has a smaller error value than the TSR linear model. The results of forecasting the stock price of PT. Telkom Indonesia Tbk for the period 02 January 2021 to 29 January 2021 formed a decreasing trend pattern.

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Komparasi Regresi Ekonometri pada Perekonomian Indonesia 2SLS, VEC, dan ARIMA

Jurnal Ekonomi dan Pembangunan Indonesia ◽

10.21002/jepi.v2i2.627 ◽

2002 ◽

Vol 2 (2) ◽

pp. 88-112

Author(s):

Henry Viriya Surya ◽

Prastowo Cahjadi

Keyword(s):

Time Series ◽

Regression Models ◽

Moving Average ◽

Strong Support ◽

Comparison Process ◽

Economic Time Series ◽

Vector Error Correction ◽

Autoregressive Integrated Moving Average ◽

Vector Error ◽

Economic Time

This paper compares three models of econometric analysis on economy, in this case the Indonesian economy. The regression models are the two stage least squares (2SLS) which has a strong support from the economic theory of aggregate expenditure, the Vector Error Correction (VEC) and Autoregressive Integrated Moving Average (ARIMA) which both comes from the time series analysis, that do not have to be economic time series. The study tries to find out which are most suitable in analyzing the time series of Indonesian economy. After all the estimation and comparison process, we finally agree that the use of those different methods must be sinchronized with the purpose of the user's study of the economic time series.

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Forecasting daily meteorological time series using ARIMA and regression models

International Agrophysics ◽

10.1515/intag-2017-0007 ◽

2018 ◽

Vol 32 (2) ◽

pp. 253-264 ◽

Cited By ~ 15

Author(s):

Małgorzata Murat ◽

Iwona Malinowska ◽

Magdalena Gos ◽

Jaromir Krzyszczak

Keyword(s):

Time Series ◽

Time Series Data ◽

Moving Average ◽

Series Data ◽

R Software ◽

Time Series Regression ◽

Climatic Zones ◽

Autoregressive Integrated Moving Average ◽

Temperature And Precipitation ◽

Auto Regressive

Abstract The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

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Application of the Autoregressive Integrated Moving Average Exogenous (ARIMAX) with Calendar Variation Effect Method for Forecasting Chocolate Data in Indonesia and the United States

Jurnal Matematika Statistika dan Komputasi ◽

10.20956/j.v18i2.18460 ◽

2022 ◽

Vol 18 (2) ◽

pp. 224-236

Author(s):

Andy Rezky Pratama Syam

Keyword(s):

United States ◽

Time Series ◽

Moving Average ◽

The United States ◽

Time Series Regression ◽

Autoregressive Integrated Moving Average ◽

Good Ability ◽

Modeling And Forecasting ◽

Time Series Method ◽

Double Exponential Smoothing

Forecasting chocolate consumption is required by producers in preparing the amount of production each month. The tradition of Valentine, Christmas and Eid al-Fitr which are closely related to chocolate makes it impossible to predict chocolate by using the Classical Time Series method. Especially for Eid al-Fitr, the determination follows the Hijri calendar and each year advances 10 days on the Masehi calendar, so that every three years Eid al-Fitr will occur in a different month. Based on this, the chocolate forecasting will show a variation calendar effect. The method used in modeling and forecasting chocolate in Indonesia and the United States is the ARIMAX (Autoregressive Integrated Moving Average Exogenous) method with Calendar Variation effect. As a comparison, modeling and forecasting are also carried out using the Naïve Trend Linear, Naïve Trend Exponential, Double Exponential Smoothing, Time Series Regression, and ARIMA methods. The ARIMAX method with Calendar Variation Effect produces a very precise MAPE value in predicting chocolate data in Indonesia and the United States. The resulting MAPE value is below 10 percent, so it can be concluded that this method has a very good ability in forecasting.

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Automatic Identification of Autoregressive Integrated Moving Average Time Series

IIE Transactions ◽

10.1080/05695558208974599 ◽

1982 ◽

Vol 14 (3) ◽

pp. 156-166 ◽

Cited By ~ 3

Author(s):

Chin-Sheng Alan Kang ◽

David D. Bedworth ◽

Dwayne A. Rollier

Keyword(s):

Time Series ◽

Moving Average ◽

Automatic Identification ◽

Autoregressive Integrated Moving Average

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Temperature time series prediction based on autoregressive integrated moving average model

Instrumentation Mesure Métrologie ◽

10.3166/i2m.17.443-453 ◽

2018 ◽

Vol 18 (3) ◽

pp. 443-453

Author(s):

Huanhuan ZHENG ◽

Yuxiu BAI ◽

Yaqiong ZHANG

Keyword(s):

Time Series ◽

Moving Average ◽

Time Series Prediction ◽

Temperature Time Series ◽

Average Model ◽

Autoregressive Integrated Moving Average ◽

Moving Average Model ◽

Temperature Time

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Intervention Modeling

10.1093/oso/9780190661557.003.0005 ◽

2017 ◽

Author(s):

Richard McCleary ◽

David McDowall ◽

Bradley J. Bartos

Keyword(s):

Time Series ◽

Autocorrelation Function ◽

Moving Average ◽

Intervention Model ◽

Noise Component ◽

Autoregressive Integrated Moving Average ◽

Noise Function ◽

Modeling Strategy ◽

The Impact ◽

True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽

10.3390/en14010141 ◽

2020 ◽

Vol 14 (1) ◽

pp. 141

Author(s):

Jacob Hale ◽

Suzanna Long

Keyword(s):

Time Series ◽

Electricity Generation ◽

Sustainability Assessment ◽

Moving Average ◽

Time Series Prediction ◽

State Level ◽

Autoregressive Integrated Moving Average ◽

Energy Transitions ◽

Univariate Time Series ◽

Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

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