Implementation of Least Square Algorithm for Stock Price Prediction

KURNIAWAN, PASCALIS ALFREDHO (2015) Implementation of Least Square Algorithm for Stock Price Prediction. Other thesis, Prodi Teknik Informatika Unika Soegijapranata.

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Abstract

Abstract — This program begins with stock data retrieval using a syntax curl. Stock data will be taken based on the choice of date for the selected user. In the syntax of such a program will take the curl site stock data that will be targeted, finance.yahoo.com and select stock data companies what want is taken, this project takes data company Indosat Tbk. and then select the historical prices. There will appear the history data's stocks many years and updated automatically. After the site is loaded, and then take what is taken from page, this program takes the CSV API to put programming language. Prediction stock prices in this program using Least Square algorithm. Least Square algorithm is the algorithm that is used to predict future data came based on previous data. The more data that will be more predictable, then the results are more accurate. Beginning with the creation of tables analysis aims to conclude all of the stock data ad made into 1 stock. Taken stock data “high” only, as it would predict the highest stock price. After the analysis then the next entry to the Least Square formula. Least Square can only be to compute predictions 1 day ahead. The end result of the program is in the form of prediction results and graph. There are two predictions for the results, i.e. Results prediction 1 day ahead and prediction a few days ahead, and also added captions up or down in the price of the stock. And then visualized in the form of a graph line and bar chart. The graph of a line and bar chart for how to actually call him stock data is the same, but are distinguished by a type, line and columns. With the graphics users can find out next day stock price developments and up or down.

Item Type: Thesis (Other)
Subjects: 400 Language
> 650 Management > 657 Accounting > Financial reports
Divisions: Faculty of Computer Science > Department of Informatics Engineering
Depositing User: Mrs Christiana Sundari
Date Deposited: 04 Nov 2015 08:41
Last Modified: 04 Nov 2015 08:41
URI: http://repository.unika.ac.id/id/eprint/5797

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