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

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.

scholarly journals The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

2019 ◽  
Vol 4 (3) ◽  
pp. 58
Author(s):  
Lu Qin ◽  
Kyle Shanks ◽  
Glenn Allen Phillips ◽  
Daphne Bernard
Keyword(s):  
Time Series ◽  
Historical Data ◽  
Moving Average ◽  
Arima Model ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Enrollment Forecasting ◽  
Moving Average Model ◽  
The Impact ◽  
Practical Impact

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

The Forecasting of the Elevator Traffic Flow Time Series Based on ARIMA and GP

2012 ◽  
Vol 588-589 ◽  
pp. 1466-1471 ◽  
Author(s):  
Jun Fang Li ◽  
Qun Zong
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Linear Models ◽  
Moving Average ◽  
Arima Model ◽  
Flow Time ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Hybrid Forecasting ◽  
Artificial Neural Network Ann

As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

scholarly journals Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling – Neural Network

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Debasis Mithiya ◽  
Lakshmikanta Datta ◽  
Kumarjit Mandal
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Group Method ◽  
Data Handling ◽  
Agricultural Economy ◽  
Autoregressive Integrated Moving Average

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT TERBANG DI PINTU KEDATANGAN BANDAR UDARA INTERNASIONAL PATTIMURA AMBON DENGAN MENGGUNAKAN METODE ARIMA BOX-JENKINS

2019 ◽  
Vol 13 (3) ◽  
pp. 135-144
Author(s):  
Sasmita Hayoto ◽  
Yopi Andry Lesnussa ◽  
Henry W. M. Patty ◽  
Ronald John Djami
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Significant Parameter ◽  
International Airport ◽  
Forecast Time

The Autoregressive Integrated Moving Average (ARIMA) model is often used to forecast time series data. In the era of globalization, rapidly progressing times, one of them in the field of transportation. The aircraft is one of the transportation that the residents can use to support their activities, both in business and tourism. The objective of the research is to know the forecasting of the number of passengers of airplanes at the arrival gate of Pattimura Ambon International Airport using ARIMA Box-Jenkins method. The best model selection is ARIMA (0, 1, 3) because it has significant parameter value and MSE value is smaller.

scholarly journals Real-Time Forecasting of COVID-19 prevalence in India using ARIMA Model

2020 ◽  
Vol 4 (10) ◽  
pp. 78-83
Keyword(s):  
Time Series ◽  
Virus Disease ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
The World ◽  
Increasing Trend ◽  
Short Term Prediction

Corona virus disease (COVID -19) has changed the world completely due to unavailability of its exact treatment. It has affected 215 countries in the world in which India is no exception where COVID patients are increasing exponentially since 15th of Feb. The objective of paper is to develop a model which can predict daily new cases in India. The autoregressive integrated moving average (ARIMA) models have been used for time series prediction. The daily data of new COVID-19 cases act as an exogenous variable in this framework. The daily data cover the sample period of 15th February, 2020 to 24th May, 2020. The time variable under study is a non-stationary series as 𝒚𝒕 is regressed with 𝒚𝒕−𝟏 and the coefficient is 1. The time series have clearly increasing trend. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction. In PACF graph. Lag 1 and Lag 13 is significant. Regressed values implies Lag 1 and Lag 13 is significant in predicting the current values. The model predicted maximum COVID-19 cases in India at around 8000 during 5thJune to 20th June period. As per the model, the number of new cases shall start decreasing after 20th June in India only. The results will help governments to make necessary arrangements as per the estimated cases. The limitation of this model is that it is unable to predict jerks on either lower or upper side of daily new cases. So, in case of jerks re-estimation will be required.

scholarly journals Use of the Autoregressive Integrated Moving Average (ARIMA) Model to Forecast Near-Term Regional Temperature and Precipitation

2020 ◽  
Vol 35 (3) ◽  
pp. 959-976 ◽  
Author(s):  
Yuchuan Lai ◽  
David A. Dzombak
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Environmental Engineering ◽  
Statistical Techniques ◽  
Engineering Applications ◽  
Autoregressive Integrated Moving Average ◽  
Temperature And Precipitation ◽  
Regional Temperature ◽  
Near Term

Abstract A data-driven approach for obtaining near-term (2–20 years) regional temperature and precipitation projections utilizing local historical observations was established in this study to facilitate civil and environmental engineering applications. Given the unique characteristics of temporal correlation and skewness exhibited in individual time series of temperature and precipitation variables, a statistical time series forecasting technique was developed based on the autoregressive integrated moving average (ARIMA) model. Annual projections obtained from the ARIMA model—depending on individual series—can be interpreted as an integration of the most recent observations and the long-term historical trend. In addition to annual temperature and precipitation forecasts, methods of estimating confidence intervals for different return periods and simulating future daily temperature and precipitation were developed to extend the applicability for use in engineering. Quantitative comparisons of annual temperature and precipitation forecasts developed from the ARIMA model and other common statistical techniques such as a linear trend method were performed. Results suggested that while the ARIMA model cannot outperform all other techniques for all evaluated climate indices, the ARIMA model in general provides more accurate projections—especially interval forecasts—and is more reliable than other common statistical techniques. With the use of the ARIMA-based statistical forecasting model, interpretable and reliable near-term, location-specific temperature and precipitation forecasts can be obtained for consideration of changing climate in civil and environmental engineering applications.

scholarly journals Analysis and Prediction of Hydraulic Support Load Based on Time Series Data Modeling

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yi-Hui Pang ◽  
Hong-Bo Wang ◽  
Jian-Jian Zhao ◽  
De-Yong Shang
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Sliding Window ◽  
Series Data ◽  
Average Model ◽  
Hydraulic Support ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.

Time Series Analysis of Pulmonary Tuberculosis Incidence: Forecasting by Applying the Time Series Model

2013 ◽  
Vol 709 ◽  
pp. 819-822 ◽  
Author(s):  
Yin Ping Chen ◽  
Ai Ping Wu ◽  
Cui Ling Wang ◽  
Hai Ying Zhou ◽  
Shu Xiu Feng
Keyword(s):  
Time Series ◽  
Pulmonary Tuberculosis ◽  
Moving Average ◽  
Arima Model ◽  
Autoregressive Integrated Moving Average ◽  
Tuberculosis Incidence ◽  
Short Lead Time ◽  
White Noise Sequence ◽  
Noise Sequence ◽  
Residual Sequence

The main objective of this study is to identify the stochastic autoregressive integrated moving average (ARIMA) model to predict the pulmonary tuberculosis incidence in Qianan. Considering the Box-Jenkins modeling approach, the incidence of pulmonary tuberculosis was collected monthly from 2004 to 2010. The model ARIMA(0,1,1)12 was established finally and the residual sequence was a white noise sequence. Then, this model was used for calculating dengue incidence for the last 6 observations compared with observed data, and performed to predict the monthly incidence in 2011. It is necessary and practical to apply the approach of ARIMA model in fitting time series to predict pulmonary tuberculosis within a short lead time.

scholarly journals Testing Weak Form of Stock Market Efficiency at The Indonesia Sharia Stock Index

2019 ◽  
Vol 10 (1) ◽  
pp. 17
Author(s):  
Isnaini Nuzula Agustin
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Moving Average ◽  
Arima Model ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Stock Index ◽  
Time Series Modelling ◽  
Current Price ◽  
Run Test

AbstractEfficient Market is the market where all traded securities prices reflects all available information. Market Efficient Hypotesis in the Weak Form stated that past stock price movement incorporated with current securities’s prices, thus it can be used to predicting the current price or return. The objective of this research is to examine the weak form of Efficient Market Hypothesis (EMH) in Indonesia Sharia Stock Index (ISSI) over the period of January 3rd2017 -February 8th 2019. To Examine the EMH, some appropriate tests are developed, these are: Run Test, Autocorrelation Test, Autoregressive Integrated Moving Average (ARIMA), and Paired Sample t-test. The result findings showing that ISSI is not efficient in the weak form during the period of the study. Moreover, in accordance with time series modelling result, the fitted model is ARIMA (1,1,1) with accuracy level of 78%. This result proved that ARIMA model successfully and accurately in forecasting ISSI indices. It can be implied that the historical stock index data in the past still described the stock index information in the future. Thus, technical analysis is still feasible to do as the guide for investors in conducting transactions in the capital market.AbstrakPasar yang efisien adalah pasar dimana semua harga sekuritas yang diperdagangkan telah mencerminkan semua informasi yang tersedia. Teori pasar efisien bentuk lemah menyatakan bahwa perubahan harga masa lalu tidak berhubungan dengan harga sekuritas sekarang, sehingga tidak dapat digunakan untuk memprediksi harga atau return dari sekuritas. Penelitian ini bertujuan untuk melakukan pengujian hipotesis pasar efisien bentuk lemah pada Indeks Saham Syariah Indonesia (ISSI). Data diambil pada periode 3 Januari 2017 – 8 Februari 2019. Pada tahap awal penelitian, Run test dan Autocorrelation test dilakukan untuk melihat apakah pasar efisien bentuk lemah berlaku pada ISSI. Selanjutnya dilakukan pembentukan pemodelan time series ARIMA untuk melihat teknik prediksi yang sesuai untuk memprediksi Indeks Saham ISSI. Hasil Run test dan Autocorrelation test menunjukkan bahwa hipotesis pasar efisien bentuk lemah tidak terbukti. Pada pembentukan model ARIMA, terlihat bahwa model yang sesuai adalah ARIMA (1,1,1) menghasilkan tingkat akurasi sebesar 78%. Hal ini membuktikan bahwa model ARIMA berhasil dan akurat digunakan untuk memprediksi Indeks Harga Saham ISSI. Oleh karena itu, analisis teknikal masih dapat digunakan oleh investor untuk menjadi pedoman dalam melakukan transaksi perdagangan di pasar modal.

scholarly journals Prediksi Pencurian Sepeda Motor Menggunakan Model Time Series (Studi Kasus: Polres Kotabumi Lampung Utara)

2020 ◽  
Vol 14 (3) ◽  
pp. 425-434
Author(s):  
MELI PRANATA ◽  
DIAN ANGGRAINI ◽  
Deden Makbuloh ◽  
Achi Rinaldi
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Model ◽  
Ar Model ◽  
Autoregressive Integrated Moving Average ◽  
Model Time Series

Tindak kriminal adalah kejahatan yang melanggar undang-undang suatu Negara atau melanggar norma yang berlaku dalam masyarakat. Pencurian merupakan salah satu bentuk dari perbuatan tindak kriminal. Dampak yang ditimbulkan dari adanya pencurian adalah perasaan kurang aman, takut, dan tenang. Salah satu model yang digunakan untuk memprediksi jumlah kasus pencurian yaitu model time series. Model time series adalah serangkaian nilai pengamatan yang diambil selama periode waktu tertentu. Pada umumnya, dalam interval-interval yang sama panjang, (Spuege & Stephens, 2004). Penelitian ini bertujuan memodelkan data tindak kriminal yang terjadi di Lampung Utara dengan model Autoregressive (AR), Moving Average (MA), dan Autoregressive Integrated Moving Average (ARIMA). Selanjutnya dari model terbaik akan digunakan untuk peramalan 6 bulan kedepan. Hasil penelitian model AR , model AR , model MA , ARIMA , dan model ARIMA . Model MA  memiliki koefisien parameter yang signifikan, memenuhi uji diagnostic tidak adanya residual pada model dan memiliki nilai RMSE dan AIC terkecil dengan nilai RMSE sebesar dan nilai AIC sebesar . Hasil prediksi model MA  untuk 6 bulan ke depan cenderung mendatar.

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