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.

Pemodelan Prakiraan Kas Pemerintah

2017 ◽  
Vol 2 (3) ◽  
pp. 1-11
Author(s):  
NFN Iskandar
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Government Policies ◽  
Forecasting Model ◽  
Cash Management ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
Greek Crisis ◽  
Artificial Neural Network Ann

ABSTRACT Government cash management refers to the strategies for managing government money to fulfil governments’ obligations effectively. Failure to manage cash effectively risks undermining the implementation of government policies. The Greek crisis in 2010 is an example of a government failing to manage resources effectively. Despite the importance of effective government cash management, the literature on effective cash forecasting, as one of effective government cash management’s pillars, in the public sector is scarce. This paper addresses this shortcoming by developing a government cash forecasting model with an accuracy that meets acceptable levels of materiality for the cash manager. Using Indonesian government expenditures data in a case study, we utilise Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to build cash forecasting models. The results provide evidence that the ANN method is superior then the ARIMA model to build a government cash forecasting model. ABSTRAK Pengelolaan Kas Pemerintah mengacu pada serangkaian strategi yang dilakukan oleh pemerintah dalam mengelola uang pemerintah secara efektif dalam rangka memenuhi kewajiban pemerintah. Kegagalan dalam mengelola uang pemerintah secara efektif beresiko mengganggu pelaksanaan kebijakan pemerintah. Krisis yang dialami Yunani di tahun 2010 merupakan salah satu contoh dampak yang dapat ditimbulkan dari tidak berhasilnya suatu pemerintahan mengelola sumber daya keuangan yang mereka milik secara efektif. Terlepas dari pentingnya mengelola kas pemerintah secara efektif, literatur tentang bagaimana menyusun prakiraan kas yang efektif – sebagai salah satu pilar Pengelolaan Kas Pemerintah – bagi sektor publik masih langka. Penelitian ini bertujuan untuk mengisi kesenjangan dalam literatur dengan memperkenalkan salah satu cara menyusun model prakiraan kas pemerintah dengan tingkat akurasi yang memenuhi harapan Pengelola Kas pemerintah. Dengan menggunakan data historis harian pengeluaran pemerintah Indonesia sebagai sebuah studi kasus, penelitian ini menggunakan Autoregressive Integrated Moving Average (ARIMA) dan Jaringan Syaraf Tiruan (JST) untuk menyusun model prakiraan kas. Penelitian ini menunjukkan bahwa penggunaan metode Jaringan Syaraf Tiruan (JST) dapat menjadi alternatif dalam menyusun model prakiraan kas pemerintah dengan tingkat akurasi model prakiraan kas yang lebih tinggi dibandingkan menggunakan ARIMA model.

scholarly journals Optimization of ARIMA Forecasting Model using Firefly Algorithm

2019 ◽  
Vol 13 (2) ◽  
pp. 127 ◽  
Author(s):  
Ilham Unggara ◽  
Aina Musdholifah ◽  
Anny Kartika Sari
Keyword(s):  
Time Series ◽  
Firefly Algorithm ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Information Criterion ◽  
Forecasting Model ◽  
Optimal Model ◽  
Autoregressive Integrated Moving Average ◽  
Foreign Tourist

 Time series prediction aims to control or recognize the behavior of the system based on the data in a certain period of time. One of the most widely used method in time series prediction is ARIMA (Autoregressive Integrated Moving Average). However, ARIMA has a weakness in determining the optimal model. firefly algorithm is used to optimize ARIMA model (p, d, q). by finding the smallest AIC (Akaike Information Criterion) value in determining the best ARIMA model. The data used in the study are daily stock data JCI period January 2013 until August 2016 and data of foreign tourist visits to Indonesia period January 1988 to November 2017.Based on testing, for JCI data, obtained predicted results with Box-Jenkins ARIMA model produces RMSE 49.72, whereas the prediction with the ARIMA Optimization model yielded RMSE 49.48. For the data of Foreign Tourist Visits, the predicted results with the Box-Jenkins ARIMA model resulted in RMSE 46088.9, whereas the predicted results with ARIMA optimization resulted in RMSE 44678.4. From these results it can be concluded that the optimization of ARIMA model with Firefly Algorithm produces better forecasting model than ARIMA model without Optimization.

Hybrid Neural Models For Rice Yields Times Forecasting

2012 ◽  
Author(s):  
Ruhaidah Samsudin ◽  
Puteh Saad ◽  
Ani Shabri
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Data Sets ◽  
Ann Model ◽  
Yield Data ◽  
Rice Yields ◽  
Artificial Neural Network Ann

In this paper, time series prediction is considered as a problem of missing value. A model for the determination of the missing time series value is presented. The hybrid model integrating autoregressive intergrated moving average (ARIMA) and artificial neural network (ANN) model is developed to solve this problem. The developed models attempts to incorporate the linear characteristics of an ARIMA model and nonlinear patterns of ANN to create a hybrid model. In this study, time series modeling of rice yield data in Muda Irrigation area. Malaysia from 1995 to 2003 are considered. Experimental results with rice yields data sets indicate that the hybrid model improve the forecasting performance by either of the models used separately. Key words: ARIMA; Box and Jenkins; neural networks; rice yields; hybrid ANN model

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.

scholarly journals SHORT-TERM TRAFFIC FLOW PREDICTION USING A METHODOLOGY BASED ON AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND GENETIC PROGRAMMING

Transport ◽  
2016 ◽  
Vol 31 (3) ◽  
pp. 343-358 ◽  
Author(s):  
Chengcheng Xu ◽  
Zhibin Li ◽  
Wei Wang
Keyword(s):  
Genetic Programming ◽  
Traffic Flow ◽  
Hybrid Model ◽  
Moving Average ◽  
Arima Model ◽  
Predictive Performance ◽  
Hybrid Models ◽  
Short Term ◽  
Time Intervals ◽  
Autoregressive Integrated Moving Average

The accurate short-term traffic flow forecasting is fundamental to both theoretical and empirical aspects of intelligent transportation systems deployment. This study aimed to develop a simple and effective hybrid model for forecasting traffic volume that combines the AutoRegressive Integrated Moving Average (ARIMA) and the Genetic Programming (GP) models. By combining different models, different aspects of the underlying patterns of traffic flow could be captured. The ARIMA model was used to model the linear component of the traffic flow time series. Then the GP model was applied to capture the nonlinear component by modelling the residuals from the ARIMA model. The hybrid models were fitted for four different time-aggregations: 5, 10, 15, and 20 min. The validations of the proposed hybrid methodology were performed by using traffic data under both typical and atypical conditions from multiple locations on the I-880N freeway in the United States. The results indicated that the hybrid models had better predictive performance than utilizing only ARIMA model for different aggregation time intervals under typical conditions. The Mean Relative Error (MRE) of the hybrid models was found to be from 4.1 to 6.9% for different aggregation time intervals under typical conditions. The predictive performance of the hybrid method was improved with an increase in the aggregation time interval. In addition, the validation results showed that the predictive performance of the hybrid model was also better than that of the ARIMA model under atypical conditions.

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 Forecasting Import Demand of Table Grapes: Empirical Evidence from Thailand

2020 ◽  
Vol 10 (2) ◽  
pp. 578-586
Author(s):  
Chalermpon Jatuporn ◽  
Patana Sukprasert ◽  
Siros Tongchure ◽  
Vasu Suvanvihok ◽  
Supat Thongkaew
Keyword(s):  
Time Series ◽  
Empirical Evidence ◽  
Unit Root ◽  
Moving Average ◽  
Unit Root Test ◽  
Import Demand ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Table Grapes ◽  
Monthly Time Series

The purpose of this study is to forecast the import demand of table grapes of Thailand using monthly time series from January 2007 to April 2020. The ADF unit root test is used for stationarity checking, and seasonal autoregressive integrated moving average (SARIMA) is applied to forecast the import demand of table grapes. The results revealed that the integration of time series was in the first difference for non-seasonal and seasonal order. The best-fitted forecasting model was SARIMA(1,1,3)(2,1,0)12. The forecasted period for the next eight months showed the import demand of table grapes of Thailand that is slightly decreased by an average of 11.398 percent, with overall expected to decrease by an average of 15.218 percent in 2020.

scholarly journals Self-Identification ResNet-ARIMA Forecasting Model

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Model Identification ◽  
Series Data ◽  
Percentage Error ◽  
Forecasting Model ◽  
Partial Autocorrelation Function

The challenging endeavor of a time series forecast model is to predict the future time series data accurately. Traditionally, the fundamental forecasting model in time series analysis is the autoregressive integrated moving average model or the ARIMA model requiring a model identification of a three-component vector which are the autoregressive order, the differencing order, and the moving average order before fitting coefficients of the model via the Box-Jenkins method. A model identification is analyzed via the sample autocorrelation function and the sample partial autocorrelation function which are effective tools for identifying the ARMA order but it is quite difficult for analysts. Even though a likelihood based-method is presented to automate this process by varying the ARIMA order and choosing the best one with the smallest criteria, such as Akaike information criterion. Nevertheless the obtained ARIMA model may not pass the residual diagnostic test. This paper presents the residual neural network model, called the self-identification ResNet-ARIMA order model to automatically learn the ARIMA order from known ARIMA time series data via sample autocorrelation function, the sample partial autocorrelation function and differencing time series images. In this work, the training time series data are randomly simulated and checked for stationary and invertibility properties before they are used. The result order from the model is used to generate and fit the ARIMA model by the Box-Jenkins method for predicting future values. The whole process of the forecasting time series algorithm is called the self-identification ResNet-ARIMA algorithm. The performance of the residual neural network model is evaluated by Precision, Recall and F1-score and is compared with the likelihood basedmethod and ResNET50. In addition, the performance of the forecasting time series algorithm is applied to the real world datasets to ensure the reliability by mean absolute percentage error, symmetric mean absolute percentage error, mean absolute error and root mean square error and this algorithm is confirmed with the residual diagnostic checks by the Ljung-Box test. From the experimental results, the new methodologies of this research outperforms other models in terms of identifying the order and predicting the future values.

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.

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