Studying Changes on Stock Market Transactions Using Different Techniques for Multivariate Time Series

Ahmed Mohamed Mohamed Elsayed

doi:10.11648/j.ajtas.20211001.18

Abstract

There are many studies dealt with univariate time series data, but the analysis of multivariate time series are rarely discussed. This article discusses the theoretical and numerical aspects of different techniques that analyze the multivariate time series data. These techniques are ANN, ARIMA, GLM and VARS models. All techniques are used to analyze the data that obtained from Egypt Stock Exchange Market. R program with many packages are used. These packages are the "neuralnet, nnet, forecast, MTS and vars". The process of measuring the accuracy of forecasting are investigated using the measures ME, ACF, MAE, MPE, RMSE, MASE, and MAPE. This is done for seasonal and non-seasonal time series data. Best ARIMA model with minimum error is constructed and tested. The lags order of the model are identified. Granger test for causality indicated that Exchange rate is useful for forecasting another time series. Also, the Instant test indicated that there is instantaneous causality between Exchange rate and other time series. For non-seasonal data, the NNAR() model is equivalent to ARIMA() model. Also, for seasonal data, the NNAR(p,P,0)[m] model is equivalent to an ARIMA(p,0,0)(P,0,0)[m] model. For these data, we concluded that the ANN and GLMs of fitting multivariate seasonal time series is better than multivariate non-seasonal time series. The transactions of Finance, Household and Chemicals sectors are significant for Exchange rate in non-seasonal time series case. The forecasts that based on stationary time series data are more smooth and accurate. VARS model is more accurate rather than VAR model for ARIMA (0,0,1). Forecasts of VAR values are predicted over short horizon, because the prediction over long horizon becomes unreliable or uniform.

Highlights

Time series analysis is one of the most important processes that many companies and even many countries need
The parameter estimates of Finance, Household and Chemicals sectors are significant for Exchange rate in non-seasonal time series case
The Artificial neural networks (ANN) model is constructed, and the accuracy values of the model is constructed using two accurate measures root of mean squared error (RMSE) and R2, and we have obtained the high accuracy in the case of considered the Exchange rate is input or output variable

Summary

Introduction

Time series analysis is one of the most important processes that many companies and even many countries need. Artificial neural networks (ANN) have become one of the most important methods of artificial intelligence in the processes of forecasting, and given that many recent articles do not deal much with the processes of multivariate analysis, whether by the autoregressive integrated moving average (ARIMA) models or ANNs models. We will combine both methods to forecast multivariate time series about applications are based on real data using some of R program packages. If we add an intermediate layer with hidden neurons, the ANN becomes non-linear This is known as a multilayer Feed-Forward ANN, where each layer of nodes receives inputs from the previous layers. We have evaluated our ANN model using the residual methods such as RMSE for the test set. [18,19,20,21]

Packages of ANN and ARIMA Time Series Models

VARS Models

Numeric Analysis

Dataset Features

ANN of Multivariate Non-time Series Data

ANN of Non-Seasonal Multivariate Time Series Data

GLM of Non-Seasonal Time Exchange Rate

VARS of Multivariate Time Series Data

Discussions

Findings

Conclusions