Univariate Financial Time Series Prediction using Clonal Selection Algorithm

Abstract

The ability to predict the financial market is beneficial not only to the individual but also to the organization and country. It is not only beneficial in terms of financial but also in terms of making a short-term and long-term decision. This paper presents an experimental study to perform univariate financial time series prediction using a clonal selection algorithm (CSA). CSA is an optimization algorithm that is based on clonal selection theory. It is a subset of the artificial immune system, a class of evolutionary algorithms inspired by the immune system of a vertebrate. Since CSA is an optimization algorithm, the univariate financial time series prediction problem was modeled into an optimization problem using a weighted regression model. CSA was used to search for the optimal set of weights for the regression model to generate prediction with the lowest error. Three data sets from the financial market were chosen for the experiments of this study namely S&P500 price, Gold price, and EUR-USD exchange rate. The performance of CSA is measured using RMSE. The value of RMSE for a problem is related to the maximum and minimum value of the data set. Therefore, the results were not compared to other data sets. Instead, it is compared to the range of values of the data sets. The result of the experiments shows that CSA can make decent predictions for financial time series despite being inferior to ARIMA. Hence, this finding implies that CSA can be implemented on a univariate financial time series prediction problem given that the problem is modeled as an optimization problem.

Application of Neural Networks in Financial Time Series Forecasting Models

“…Syriopoulos et al [15] proposed a fuzzy GARCH modeling method to predict stock market returns, which considers time-varying fluctuations and adjusts the adaptability of the model through gradual model construction. Cheng et al used support vector machine to forecast the Korea Composite Stock Price Index (KOSPI) and showed better prediction performance than BP neural network and case-based inference methods [16]. Xiangfei Li et al combined both support vector machine and empirical mode decomposition (EMD) methods to predict the error sequence of the initial forecast set and correct the original forecast value using the error forecast value.…”

Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting

“…Their study suggests that CNN-based models performed better than LSTM based models in forecasting stock price in the context of India's National Stock Exchange (NSE). Azlan et al [3] conducted a time series analysis using the clonal selection algorithm and found almost similar forecasting performance as ARIMA models on yahoo stock price. In time series forecasting, LSTM models illustrated superior forecasting performance with a long-term confident band [4].…”

“…Figure 1 illustrates the overall structure of our experiment design based on Alinma dataset and then we replicate the same procedure for the other selected datasets. A sliding window of size five is commonly used in forecasting stock price, mentioned in the surveyed literature [3], [4], [8]. As a result, the feature list contains stock prices of t-4, t-3, t-2, t-1, t days where t corresponds to today.…”

Implementasi Algoritma Depth First Search Dalam Penyelesaian Permasalahan Lintasan dan Sirkuit Euler

“…Awalnya komputer digunakan untuk menghitung atau alat hitung sederhana [5]- [7]. Teknologi yang digunakan dalam komputer berkembang pesat dan hingga saat ini AI sering digunakan untuk mencari penyelesaian atau solusi [8]- [10]. Dalam suatu kasus AI terdapat suatu ruang yang disebut ruang keadaan.…”