Comparative Analysis of ARIMA Model and Neural Network in Predicting Stock Price

2020 ◽  
pp. 436-445
Author(s):  
Enping Yu
Keyword(s):  
Neural Network ◽  
Comparative Analysis ◽  
Stock Price ◽  
Arima Model

scholarly journals Autoregressive Neural Network EURO STOXX 50 Forecasting Model Based on Principal Component Stock Selection

2021 ◽  
Vol 6 (2) ◽  
pp. 71-81
Author(s):  
Ahmad Abu Alrub ◽  
Tahir Abu Awwad ◽  
Emad Al-Saadi
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Stock Prices ◽  
Stock Price ◽  
Arima Model ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Model Based ◽  
Index Stock ◽  
Autoregressive Neural Network

Purpose: The given study looks into forecast accuracy of a traditional ARIMA model while comparing it to Autoregressive Neural Network (AR-NN) model for 984 trading days on EURO STOXX 50 Index. Methodology: A hybrid model is constructed by combining ARIMA model and feed-forward neural network model aiming to attain linear and non-linear price fluctuations. The study also incorporates the investigation of component stock prices of the index, that can be selected to improve the predictability of the hybrid model.  Findings:The reached ARIMA (1,1,3) model showed higher scores than AR-NN model however integrating selected exogenous stock prices from the index components gave much notable accuracy results. The selected exogenous stocks were extracted after conducting PCA and model scores were compared via MAPE and RMSE. Unique contribution to theory, practice and policy: The major contribution of this work is to provide the researcher and fnancial analyst a systematic approach for development of intelligent methodology to forecast stock market. This paper also presents the  outlines of proposed work with the aim to enhance the performance of existing techniques. Therefore, Empirical analysis is employed along with a hybrid model based on a feed-forward Neural Network. Lesser error is attained on the test set of Index stock price by comparing the performance of ARIMA and AR-NN while forecasting. Hence, The components of extracted Index stock price like exogenous features are added to make an influence from the AR-NN model. 

ANALISA PREDIKSI INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN METODE ARIMA

2020 ◽  
Vol 2 (2) ◽  
pp. 454
Author(s):  
Julkifli Purnama ◽  
Ahmad Juliana
Keyword(s):  
Capital Market ◽  
Stock Price ◽  
Time Series Data ◽  
Arima Model ◽  
Secondary Data ◽  
Actual Data ◽  
Series Data ◽  
Analysis Technique ◽  
The Right ◽  
The Capital Market

Investment in the capital market every manager needs to analyze to make decisions so that the right target to produce profits in accordance with what is expected. For that, we need a way to predict the decisions that will be taken in the future. The research objective is to find the best model and forecasting of the composite stock price index (CSPI). Data analysis technique The ARIMA Model time series data from historical data is the basis for forecasting. Secondary data is the closing price of the JCI on July 16 2018 to July 16 2019 to see how accurate the forecasting is done on the actual data at that time. The results of the study that the best Arima model is Arima 2.1.2 with an R-squared value of 0.014500, Schwarz criterion 10.83497 and Akaike info criterion of 10.77973. Results of forecasting actual data are 6394,609, dynamic forecast 6387,551 selisish -7,05799, statistics forecas 6400,653 difference of 6,043909. For investors or the public can use the ARIMA method to be able to predict or predict the capital market that will occur in the next period.

Maximal Overlap Discrete Wavelet Transform, Graph Theory And Backpropagation Neural Network In Stock Market Forecasting

2018 ◽  
Vol 5 (1) ◽  
pp. 41-46
Author(s):  
Rosalina Rosalina ◽  
Hendra Jayanto
Keyword(s):  
Neural Network ◽  
Graph Theory ◽  
Wavelet Transform ◽  
Stock Market ◽  
Discrete Wavelet Transform ◽  
Stock Price ◽  
Moving Average ◽  
Discrete Wavelet ◽  
Stock Market Forecasting ◽  
Artificial Neural

The aim of this paper is to get high accuracy of stock market forecasting in order to produce signals that will affect the decision making in the trading itself. Several experiments by using different methodologies have been performed to answer the stock market forecasting issues. A traditional linear model, like autoregressive integrated moving average (ARIMA) has been used, but the result is not satisfactory because it is not suitable for model financial series. Yet experts are likely observed another approach by using artificial neural networks. Artificial neural network (ANN) are found to be more effective in realizing the input-output mapping and could estimate any continuous function which given an arbitrarily desired accuracy. In details, in this paper will use maximal overlap discrete wavelet transform (MODWT) and graph theory to distinguish and determine between low and high frequencies, which in this case acted as fundamental and technical prediction of stock market trading. After processed dataset is formed, then we will advance to the next level of the training process to generate the final result that is the buy or sell signals given from information whether the stock price will go up or down.

Valid Signature Detection and Verification for Security of Individual Person

2016 ◽  
Vol 1 (1) ◽  
pp. 50-53 ◽  
Author(s):  
Varun Sharma ◽  
Narpat Singh
Keyword(s):  
Neural Network ◽  
Comparative Analysis ◽  
Recent Work ◽  
Research Work ◽  
Early Research ◽  
Individual Person ◽  
Signature Detection ◽  
Online Signature ◽  
The Right ◽  
Handwritten Signature

In the recent research work, the handwritten signature is a suitable field to detection of valid signature from different environment such online signature and offline signature. In early research work, a lot of unauthorized person put the signature and theft the data in illegal manner from organization or industries. So we have to need identify, the right person on the basis of various parameters that can be detected. In this paper, we have proposed two methods namely LDA and Neural Network for the offline signature from the scan signature image. For efficient research, we have focused the comparative analysis in terms of FRR, SSIM, MSE, and PSNR. These parameters are compared with the early work and the recent work. Our proposed work is more effective and provides the suitable result through our method which leads to existing work. Our method will help to find legal signature of authorized use for security and avoid illegal work.

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

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