scholarly journals A forecasting of stock trading price using time series information based on big data

2021 ◽  
Vol 11 (3) ◽  
pp. 2548
Author(s):  
Soo-Tai Nam ◽  
Chan-Yong Jin ◽  
Seong-Yoon Shin
Keyword(s):  
Time Series ◽  
Big Data ◽  
Stock Price ◽  
Euclidean Distance ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Tool ◽  
Large Set ◽  
Data Generation ◽  
Management Tools

Big data is a large set of structured or unstructured data that can collect, store, manage, and analyze data with existing database management tools. And it means the technique of extracting value from these data and interpreting the results. Big data has three characteristics: The size of existing data and other data (volume), the speed of data generation (velocity), and the variety of information forms (variety). The time series data are obtained by collecting and recording the data generated in accordance with the flow of time. If the analysis of these time series data, found the characteristics of the data implies that feature helps to understand and analyze time series data. The concept of distance is the simplest and the most obvious in dealing with the similarities between objects. The commonly used and widely known method for measuring distance is the Euclidean distance. This study is the result of analyzing the similarity of stock price flow using 793,800 closing prices of 1,323 companies in Korea. Visual studio and Excel presented calculate the Euclidean distance using an analysis tool. We selected “000100” as a target domestic company and prepared for big data analysis. As a result of the analysis, the shortest Euclidean distance is the code “143860” company, and the calculated value is “11.147”. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.

scholarly journals A Time-Series Data Generation Method to Predict Remaining Useful Life

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1115
Author(s):  
Gilseung Ahn ◽  
Hyungseok Yun ◽  
Sun Hur ◽  
Si-Yeong Lim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Series Data ◽  
Generation Model ◽  
Data Generation ◽  
Training Time ◽  
Symbolic Aggregate Approximation ◽  
Useful Life ◽  
Occurrence Patterns

Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to train a model without overfitting can be challenging. Here, we propose a method of generating time-series data for RUL models to resolve the problems posed by insufficient data. The proposed method converts every training time series into a sequence of alphabetical strings by symbolic aggregate approximation and identifies occurrence patterns in the converted sequences. The method then generates a new sequence and inversely transforms it to a new time series. Experiments with various RUL prediction datasets and ML/DL models verified that the proposed data-generation model can help avoid overfitting in RUL prediction model.

scholarly journals Analisis Intervesi Fungsi Step Efek Program Tol-Laut Terhadap Pergerakan Harga Saham TMAS.JK

2019 ◽  
Vol 5 (01) ◽  
pp. 47-54
Author(s):  
Wigid Hariadi
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Time Series Data ◽  
Vice President ◽  
Step Function ◽  
Series Data ◽  
Intervention Analysis ◽  
Analysis Model ◽  
External Events ◽  
Port Construction

Abstract. Intervention analysis is used to evaluate the effect of external events on a time series data. Sea-highway program is one of the leading programs Joko Widodo-Jusuf Kalla in presidential election 2014. So the author want to modeling the effect from Sea-highway programs on stock price movement in the shipping sector, TMAS.JK (Pelayaran Tempuran Emas tbk). After analyzing, proven that it has happened intervention on movement of daily stock price TMAS.JK caused by Sea-highway programs. Intervention I, on 11 August 2014, which was efect as a result of the election of the Joko Widodo-Jusuf kalla pair as President and vice President Republic of Indonesia on 22 july 2014. Intervention II, on 10 november 2014, president Joko Widodo speech in APEC about Sea-highway Program, and offering investment in port construction to foreign country. So that the model of time series analysis that right is intervention analysis model multi input step function, where the model is ARIMA (2,1,0), StepI (b=0, s=2, r=1), StepII (b=3, s=0, r=1).  Keywords: Intervention Analysis, Multi Input, Step Function, Sea-highway.    Abstrak. Analisis intervensi digunakan untuk mengevaluasi efek dari peristiwa eksternal pada suatu data time series. Program Tol-Laut merupakan salah satu program unggulan pasangan Joko Widodo-Jusuf Kalla dalam pemilu 2014. sehingga, penulis ingin memodelkan efek dari Program Tol-Laut terhadap pergerakan harga saham dibidang pelayaran, TMAS.JK (Pelayaran Tempuran Emas tbk). Setelah dilakukan analisis data, terbukti bahwa terjadi intervensi pada pergerakan harga saham harian TMAS.JK yang disebabkan oleh efek dari program Tol-Laut. Dimana intervensi I, pada tanggal 11 Agustus 2014, yang diduga sebagai dampak dari terpilihnya pasangan Joko widodo-Jusuf Kalla sebagai presiden dan wakil presiden Republik Indonesia pada tanggal 22 Juli 2014. Intervensi II, pada tanggal 10 November 2014, pidato Presiden Joko Widodo di forum APEC mengenai program  tol  laut, dan  menawarkan investasi dibidang pembangunan pelabuhan  kepada bangsa asing. Sehingga model analisis time series yang tepat adalah model analisis intervensi multi input fungsi step, dimana modelnya adalah ARIMA (2,1,0), StepI (b=0, s=2, r=1), StepII (b=3, s=0, r=1). Kata kunci: Analisis intervensi, Multi Input, fungsi step, Tol-Laut.

scholarly journals A Predictive Meta Model for Forecasting Stock Price using Time Series Data

2021 ◽  
Vol 10 (1) ◽  
pp. 345-356
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Stock Exchange ◽  
Series Data ◽  
Meta Model ◽  
Price Range ◽  
Stock Traders ◽  
The Right

Stock market prediction through time series is a challenging as well as an interesting research areafor the finance domain, through which stock traders and investors can find the right time to buy/sell stocks. However, various algorithms have been developed based on the statistical approach to forecast the time series for stock data, but due to the volatile nature and different price ranges of the stock price one particular algorithm is not enough to visualize the prediction. This study aims to propose a model that will choose the preeminent algorithm for that particular company’s stock that can forecastthe time series with minimal error. This model can assist a trader/investor with or without expertise in the stock market to achieve profitable investments. We have used the Stock data from Stock Exchange Bangladesh, which covers 300+ companies to train and test our system. We have classified those companies based on the stock price range and then applied our model to identify which algorithm suites most for a particular range of stock price. Comparative forecasting results of all algorithms in diverse price ranges have been presented to show the usefulness of this Predictive Meta Model

HUBUNGAN KAUSALITAS HARGA JUAL DAN HARGA SEWA APARTEMEN DI JABODETABEK

2021 ◽  
Vol 1 (1) ◽  
pp. 93-105
Author(s):  
Zainal Zawir Simon ◽  
Effendy Zain ◽  
Zulihar Zulihar
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Series Data ◽  
Investment Analysis ◽  
Series Data ◽  
Analysis Tool ◽  
Selling Price ◽  
Causality Analysis ◽  
Granger Causality Analysis ◽  
Causality Relationship

Abstrak Penelitian ini bertujuan untuk mengetahui hubungan kausalitas antara harga jual apartemen dan harga sewa apartemen di wilayah Jabodetabek. Data yang dipergunakan adalah data  time series dalam bentuk kuartalan untuk periode 2007:1-2018:3 dan alat analisis yang dipergunakan adalah analisa kausalitas Granger. Hasil penelitian menunjukkan bahwa tidak terdapat hubungan kausalitas antara harga jual apartemen dan harga sewa apartemen di wilayah Jabodetabek. Dengan kata lain perubahan harga jual  tidak mempengaruhi harga sewa. Sebaliknya harga sewa juga tidak mempengaruhi harga jual apartemen. Dengan demikian Investor diharapkan dalam melakukan analisis investasinya memasukkan faktor-faktor lain yang dapat mempengaruhi harga jual dan harga sewa untuk apartemen, agar terlepas dari pandangan bahwa harga jual mempengaruhi harga sewa dan sebaliknya.Kata Kunci : Harga Jual apartemen, Harga Sewa Apartemen, Data Runtut Waktu, Analisa Kausalitas GrangerABSTRACTThis study aims to determine the causality relationship between the selling price of apartments and apartment rental prices in the Greater Jakarta area. The data used are time series data in quarterly form for the period 2007: 1-2018: 3 and the analysis tool used is the Granger causality analysis. The results showed that there was no causality relationship between apartment selling prices and apartment rental prices in the Greater Jakarta area. In other words, changes in selling prices do not affect rental prices. Conversely the rental price also does not affect the selling price of the apartment. Thus Investors are expected to carry out investment analysis to include other factors that can affect the selling price and rental price for an apartment, so that regardless of the view that the selling price affects the rental price and vice versa.Keywords : Selling Price of apartments, rental prices apartments, time series data, Granger Causality Analysis

scholarly journals PhilDB - The time series database with built-in change logging

2015 ◽  
Author(s):  
Andrew MacDonald
Keyword(s):  
Time Series ◽  
Big Data ◽  
Open Source ◽  
High Performance ◽  
Time Series Data ◽  
Handling Time ◽  
Series Data ◽  
Meta Data ◽  
Static Data ◽  
Data Tracking

PhilDB is an open-source time series database. It supports storage of time series datasets that are dynamic, that is recording updates to existing values in a log as they occur. Recent open-source systems, such as InfluxDB and OpenTSDB, have been developed to indefinitely store long-period, high-resolution time series data. Unfortunately they require a large initial installation investment before use because they are designed to operate over a cluster of servers to achieve high-performance writing of static data in real time. In essence, they have a ‘big data’ approach to storage and access. Other open-source projects for handling time series data that don’t take the ‘big data’ approach are also relatively new and are complex or incomplete. None of these systems gracefully handle revision of existing data while tracking values that changed. Unlike ‘big data’ solutions, PhilDB has been designed for single machine deployment on commodity hardware, reducing the barrier to deployment. PhilDB eases loading of data for the user by utilising an intelligent data write method. It preserves existing values during updates and abstracts the update complexity required to achieve logging of data value changes. PhilDB improves accessing datasets by two methods. Firstly, it uses fast reads which make it practical to select data for analysis. Secondly, it uses simple read methods to minimise effort required to extract data. PhilDB takes a unique approach to meta-data tracking; optional attribute attachment. This facilitates scaling the complexities of storing a wide variety of data. That is, it allows time series data to be loaded as time series instances with minimal initial meta-data, yet additional attributes can be created and attached to differentiate the time series instances as a wider variety of data is needed. PhilDB was written in Python, leveraging existing libraries. This paper describes the general approach, architecture, and philosophy of the PhilDB software.

scholarly journals Implementation of Long Short-Term Memory and Gated Recurrent Units on Grouped Time-Series Data to Predict Stock Prices Accurately

2021 ◽  
Author(s):  
Armin Lawi ◽  
Hendra Mesra ◽  
Supri Amir
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Percentage Error ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract Stocks are an attractive investment option since they can generate large profits compared to other businesses. The movement of stock price patterns on the stock market is very dynamic; thus it requires accurate data modeling to forecast stock prices with a low error rate. Forecasting models using Deep Learning are believed to be able to accurately predict stock price movements using time-series data, especially the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. However, several previous implementation studies have not been able to obtain convincing accuracy results. This paper proposes the implementation of the forecasting method by classifying the movement of time-series data on company stock prices into three groups using LSTM and GRU. The accuracy of the built model is evaluated using loss functions of Rooted Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results showed that the performance evaluation of both architectures is accurate in which GRU is always superior to LSTM. The highest validation for GRU was 98.73% (RMSE) and 98.54% (MAPE), while the LSTM validation was 98.26% (RMSE) and 97.71% (MAPE).

Application of Fuzzy Sets and Shadowed Sets in Predicting Time Series Data

2018 ◽  
pp. 297-315
Author(s):  
Mahua Bose ◽  
Kalyani Mali
Keyword(s):  
Time Series ◽  
Stock Price ◽  
Time Series Data ◽  
Forecast Accuracy ◽  
Positional Information ◽  
Data Partitioning ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Shadowed Set ◽  
Shadowed Sets

In recent years, several methods for forecasting fuzzy time series have been presented in different areas, such as stock price, student enrollments, climatology, production sector, etc. Choice of data partitioning technique is a central factor and it highly influences the forecast accuracy. In all existing works on fuzzy time series model, cluster with highest membership is used to form fuzzy logical relationships. But the position of the element within the cluster is not considered. The present study incorporates the idea of fuzzy discretization and shadowed set theory in defining intervals and uses the positional information of elements within a cluster in selection of rules for decision making. The objective of this work is to show the effect of the elements, lying outside the core area on forecast. Performance of the presented model is evaluated on standard datasets.

Analysis of The Influence of Return on Equity on Sharia Stock Price Moderated by Corporate Social Responsibility

2021 ◽  
Vol 5 (1) ◽  
pp. 72
Author(s):  
Zikra Agusti Humaira
Keyword(s):  
Time Series ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Cross Section ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Return On Equity ◽  
Purposive Sampling ◽  
Corporate Social

<em>This research aims to determine the effect of Return On Equity (ROE) on Sharia Share Prices of the Property Sector Moderated by Corporate Social Responsibility (CSR). The population of this research is all property sector companies listed on the Indonesia Stock Exchange from 2011 to 2017. The research sample was taken using a purposive sampling method, so that a sample of 12 companies was obtained. The data analysis technique used is the panel data regression method which is a combination of cross section data and time series data using Eviews 7 software. The results of the analysis show Return On Equity (ROE) does not have a significant effect on the price of sharia shares in the property sector and Corporate Social Responsibility (CSR) is proven to strengthen the effect of ROE on the sharia stock price of the property sector. The findings of this study will be useful for relevant policy makers because disclosure of CSR in financial statements will provide positive sentiment that will increase investor confidence to invest.</em><br /><em><br /></em>Penelitin ini bertujuan untuk mengetahui pengaruh <em>Return On Equity</em> (ROE) Terhadap Harga Saham Syariah Sektor Properti Yang Dimoderasi Oleh <em>Corporate Social Responsibility</em> (CSR). Populasi penelitian ini adalah seluruh perusahaan sektor properti yang terdaftar di Bursa Efek Indonesia tahun 2011 hingga 2017. Sampel penelitian diambil menggunakan metode <em>purposive sampling</em>, sehingga diperoleh sampel sebanyak 12 perusahaan. Teknik analisis data yang digunakan adalah metode regresi data panel yang merupakan gabungan dari data <em>cross section</em> dan data <em>time series</em> dengan menggunakan software <em>Eviews 7</em>. Hasil analisis menunjukkan <em>Return On Equity </em>(ROE) tidak memiliki pengaruh yang signifikan terhadap harga saham syariah sektor properti dan <em>Corporate Social Responsibility </em>(CSR) terbukti memperkuat pengaruh ROE terhadap harga saham syariah sektor properti. Temuan penelitian ini akan bermanfaat bagi pengambil kebijakan terkait karena pengungkapkan CSR dalam laporan keuangan akan menjadi nilai yang akan menambah kepercayaan investor untuk melakukan investasi.<br /><strong>Kata Kunci : </strong>Harga Saham Sektor Properti, ROE, CSR

Sign in / Sign up

Export Citation Format

Share Document