Analysing and Forecasting Share Price Index in Malaysia

2019 ◽  
Vol 16 (12) ◽  
pp. 4930-4936
Author(s):  
Nur Afiqah Mohamed Hafiz ◽  
Norizarina Ishak ◽  
Ahmad Fadly Nurullah Rasedee
Keyword(s):  
Time Series ◽  
Price Index ◽  
Moving Average ◽  
Arima Model ◽  
Investment Model ◽  
Share Price ◽  
Stationary Time Series ◽  
Forecasting Model ◽  
Yield Model ◽  
Index Model

Wilkie investment model is a stochastic investment model that was built by Wilkie in 1984 and was updated in 1995. The model building objective is forecasting. Box-Jenkins method was the basic structure of Wilkie model. It involves various type of forecasting model. Some model handle stationary time series such as autoregressive moving average (ARMA) model while some of them handle non-stationary time series such as autoregressive integrated moving average (ARIMA) model. There are four sub-models in the Wilkie model which is retail price index model, share dividend yield model, share dividend index model and Consols yield model. In this paper, the Wilkie share price model [4] was apply to Malaysia data in analysing and forecasting FTSE Bursa Malaysia KLCI share price index for 36 month ahead from November 2015 to October 2018. Monthly historical data from January 1996 to October 2015 are use as the base. We use ARIMA model to forecast the share price index in Malaysia. ARIMA(0,1,2) model was chosen as the best fit forecasting model. Through forecasting, we are able to evaluate the performance of the share price index in Malaysia.

scholarly journals PERAMALAN LAJU INFLASI DENGAN METODE AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

2017 ◽  
Vol 14 (4) ◽  
pp. 524 ◽  
Author(s):  
Djawoto Djawoto
Keyword(s):  
Time Series ◽  
Price Index ◽  
Inflation Rate ◽  
Moving Average ◽  
Arima Model ◽  
Consumer Price Index ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Auto Regressive ◽  
Auto Regression

Auto Regression Integrated Moving Average (ARIMA) or the combination model of Auto Regression with moving average, is a linier model which is able to represent the stationary time series or non stationary time series. The purpose of this research is to forecast the inflation rate in November 2010 with the Consumer Price Index (CPI) by using ARIMA. The inflation indicator is very important to anticipate in making the Government’s policy and decision as well as for the citizen is for the information to determine what to do in related with savings and investment. By looking at the existing criteria, it is determined that the best model is ARIMA (1,1,0) or AR (1). Model ARIMA (1,1,0), the coefficient value AR (1) is significant,which has the most minimum value of Akaike Info Criterion (AIC) and Schwars Criterion (SC) compare toARIMA (0,1,1) or MA (1) and ARIMA (1,1,1) or AR (1) MA (1). In summarize, the ARIMA model used to forecast the valueof IHK is ARIMA (1,1,0).

scholarly journals PERAMALAN LAJU INFLASI DENGAN METODE AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA)

2018 ◽  
Vol 14 (4) ◽  
pp. 524-538
Author(s):  
Djawoto Djawoto
Keyword(s):  
Time Series ◽  
Price Index ◽  
Inflation Rate ◽  
Moving Average ◽  
Arima Model ◽  
Consumer Price Index ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Auto Regressive ◽  
Auto Regression

Auto Regression Integrated Moving Average (ARIMA) or the combination model of Auto Regression with moving average, is a linier model which is able to represent the stationary time series or non stationary time series. The purpose of this research is to forecast the inflation rate in November 2010 with the Consumer Price Index (CPI) by using ARIMA. The inflation indicator is very important to anticipate in making the Government’s policy and decision as well as for the citizen is for the information to determine what to do in related with savings and investment. By looking at the existing criteria, it is determined that the best model is ARIMA (1,1,0) or AR (1). Model ARIMA (1,1,0), the coefficient value AR (1) is significant,which has the most minimum value of Akaike Info Criterion (AIC) and Schwars Criterion (SC) compare toARIMA (0,1,1) or MA (1) and ARIMA (1,1,1) or AR (1) MA (1). In summarize, the ARIMA model used to forecast the valueof IHK is ARIMA (1,1,0).

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 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 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.

scholarly journals Predicting South Africa’s Daily COVID-19 Cases using ARIMA Forecasting Model: 6 March to 6 July 2020

2021 ◽  
Author(s):  
Shoko Claris ◽  
Chikobvu Delson
Keyword(s):  
South Africa ◽  
Time Series ◽  
Moving Average ◽  
Health Planning ◽  
Arima Model ◽  
Stationary Time Series ◽  
Online Data ◽  
Continuous Increase ◽  
Constant Variance ◽  
Using Data

Abstract Background and Objective: The COVID-19 pandemic caused approximately 11,421,822 laboratory confirmed cases globally with 196,750 confirmed cases in South Africa by the 6th of July 2020. Coronavirus is transmitted from one person to another even before any symptoms appear, thus posing a severe threat to the society as a whole. This study is aimed at coming up with an ARIMA model to predict daily COVID-19 disease cases in South Africa using data from online sources. Materials and Methods: The study used online data on daily COVID-19 reported cases in South Africa (SA) recorded from 6 March 2020 to the 6th of July 2020. Time series analysis is used to investigate the trend in the daily COVID-19 disease cases leading to the Auto-Regressive Integrated Moving Average (ARIMA) model. Results: The time plot of the series suggests the need for differencing of the data up to the second-order to achieve a stationary time series. The best candidate model was an ARIMA(7,2,0). Residuals for the selected model are non-correlated and normally distributed with mean zero with a constant variance as expected in a good model. The fitted model predicted a continuous increase in the daily COVID-19 disease cases for the next 20 days ahead to day 143 with slight falls at a few time points.Conclusion: The results showed that ARIMA models can be applied to COVID-19 patterns in South Afriva. The model forecasted a continuous increase in the daily COVID-19 cases in South Africa. These results are important for public health planning in order combat the pandemic.

scholarly journals An Application of Time Series ARIMA Forecasting Model for Predicting the Ringgit Malaysia-Dollar Exchange Rate

2018 ◽  
Vol 1 (1) ◽  
pp. 42-48
Author(s):  
Hanisah Hanun Muhamad Hatta ◽  
Faezzah Mohd Daud ◽  
Norsyafiqah Mohamad
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Moving Average ◽  
Arima Model ◽  
Secondary Data ◽  
Current Data ◽  
Forecasting Model ◽  
Dollar Exchange Rate ◽  
Future Performance ◽  
Auto Regression

ABSTRAK. Model ARIMA yang dilambangkan sebagai ARIMA (p, d, q), pada dasarnya dari Auto Regression Moving Average (ARMA) dengan proses differencing. Objek utama untuk melakukan proses ARIMA adalah memprediksi kinerja masa depan data tertentu, dengan melakukan differencing terhadap data yang jelas atau saat ini. Prediksi dihitung untuk memiliki data yang lebih baik untuk time series berikutnya. Agar memiliki data yang baik dan sempurna, ubah data non-stasioner menjadi data stasioner. Adalah mungkin untuk memiliki lebih dari satu kali proses pembedaan untuk menciptakan model ARIMA terbaik. Tulisan ini untuk menunjukkan salah satu aplikasi time series ARIMA melalui nilai tukar ringgit Malaysia terhadap dollar. Data sebelumnya yang diambil dari data sekunder adalah dari Januari 2015 hingga Desember 2017 dengan data yang disediakan setiap minggu, yang merupakan data yang dikumpulkan setiap hari Jumat. Jadi jumlah data atau observasi selama tiga tahun adalah 161. Oleh karena itu, kita bisa melakukan prediksi berdasarkan data tersebut. ABSTRACT. Time series Auto regression Integrated Moving Average (ARIMA) model, that denoted as ARIMA (p, d, q), is basically from Auto regression Moving Average (ARMA) with differencing process. The main object to do ARIMA process is to predict the future performance of certain data, by doing the differencing towards the obvious or current data. The prediction is calculated to have the better data for the next time series. In order to have a good and perfect data, transform the non-stationary data to stationary one. It is possible to have more than one time differencing process to create the best ARIMA model. This writing is to show one of the applications of time series ARIMA through the exchange rate of ringgit Malaysia to dollar. The previous data that was taken from the secondary data is from January 2015 to December 2017 with the data provided weekly, which is the data was collected on every Friday. So the number of data or observations for three years is 161. Hence, we can do the prediction based on the data.

scholarly journals RESEARCH ON THE COMBUSTION PROCESS USING TIME SERIES

2020 ◽  
Vol 10 (2) ◽  
pp. 52-55
Author(s):  
Żaklin Grądz
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Combustion Process ◽  
Measurement Data ◽  
Stationary Time Series ◽  
Autoregressive Integrated Moving Average ◽  
One Dimensional ◽  
And Control ◽  
Stationary Form

In the combustion process, one of the most important tasks is related to maintaining its stability. Numerous methods of monitoring, diagnostics, and analysis of the measurement data are used for this purpose. The information recorded in the combustion chamber constitute one-dimensional time series. In the case of non-stationary time series, which can be transformed into the stationary form, the autoregressive integrated moving average process can be employed. The paper presented the issue of forecasting the changes in flame luminosity. The investigations discussed in the work were carried out with the ARIMA model (p,d,q). The presented forecasts of changes in flame luminosity reflect the actual processes, which enables to employ them in diagnostics and control of the combustion process.

scholarly journals Identification of monthly municipal water demand system based on autoregressive integrated moving average model tuned by particle swarm optimization

2017 ◽  
Vol 19 (2) ◽  
pp. 261-281 ◽  
Author(s):  
Sahbi Boubaker
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Water Demand ◽  
Moving Average ◽  
Arima Model ◽  
Particle Swarm ◽  
Demand System ◽  
Swarm Optimization ◽  
Municipal Water ◽  
Average Absolute Relative Error

In this paper, a modeling-identification approach for the monthly municipal water demand system in Hail region, Saudi Arabia, is developed. This approach is based on an auto-regressive integrated moving average (ARIMA) model tuned by the particle swarm optimization (PSO). The ARIMA (p, d, q) modeling requires estimation of the integer orders p and q of the AR and MA parts; and the real coefficients of the model. More than being simple, easy to implement and effective, the PSO-ARIMA model does not require data pre-processing (original time-series normalization for artificial neural network (ANN) or data stationarization for traditional stochastic time-series (STS)). Moreover, its performance indicators such as the mean absolute percentage error (MAPE), coefficient of determination (R2), root mean squared error (RMSE) and average absolute relative error (AARE) are compared with those of ANN and STS. The obtained results show that the PSO-ARIMA outperforms the ANN and STS approaches since it can optimize simultaneously integer and real parameters and provides better accuracy in terms of MAPE (5.2832%), R2 (0.9375), RMSE (2.2111 × 105m3) and AARE (5.2911%). The PSO-ARIMA model has been implemented using 69 records (for both training and testing). The results can help local water decision makers to better manage the current water resources and to plan extensions in response to the increasing need.

Towards optimal ATM cash replenishment using time series analysis

2021 ◽  
pp. 1-13
Author(s):  
Muhammad Rafi ◽  
Mohammad Taha Wahab ◽  
Muhammad Bilal Khan ◽  
Hani Raza
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Automatic Teller Machine ◽  
Proposed Model ◽  
Auto Regressive ◽  
Specific Amount

Automatic Teller Machine (ATM) are still largely used to dispense cash to the customers. ATM cash replenishment is a process of refilling ATM machine with a specific amount of cash. Due to vacillating users demands and seasonal patterns, it is a very challenging problem for the financial institutions to keep the optimal amount of cash for each ATM. In this paper, we present a time series model based on Auto Regressive Integrated Moving Average (ARIMA) technique called Time Series ARIMA Model for ATM (TASM4ATM). This study used ATM back-end refilling historical data from 6 different financial organizations in Pakistan. There are 2040 distinct ATMs and 18 month of replenishment data from these ATMs are used to train the proposed model. The model is compared with the state-of- the-art models like Recurrent Neural Network (RNN) and Amazon’s DeepAR model. Two approaches are used for forecasting (i) Single ATM and (ii) clusters of ATMs (In which ATMs are clustered with similar cash-demands). The Mean Absolute Percentage Error (MAPE) and Symmetric Mean Absolute Percentage Error (SMAPE) are used to evaluate the models. The suggested model produces far better forecasting as compared to the models in comparison and produced an average of 7.86/7.99 values for MAPE/SMAPE errors on individual ATMs and average of 6.57/6.64 values for MAPE/SMAPE errors on clusters of ATMs.

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