scholarly journals Stock Price Forecasting: Review and Experiment on Time Series Data Processing

2020 ◽  
Vol 9 (5) ◽  
pp. 7852-7858
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Stock Price ◽  
Time Series Data ◽  
Series Data ◽  
Price Forecasting ◽  
Stock Price Forecasting

scholarly journals A CNN-LSTM-Based Model to Forecast Stock Prices

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wenjie Lu ◽  
Jiazheng Li ◽  
Yifan Li ◽  
Aijun Sun ◽  
Jingyang Wang
Keyword(s):  
Time Series ◽  
Stock Prices ◽  
Stock Price ◽  
Time Series Data ◽  
Short Term Memory ◽  
Memory Function ◽  
Series Data ◽  
Price Forecasting ◽  
Stock Price Forecasting ◽  
Forecasting Method

Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory (LSTM) which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data.

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

Theoretical Consideration on the Application of Continuous Wavelet Transform to Time-Series Processing for Magnetotelluric Data

2020 ◽  
Author(s):  
Hiroki Ogawa ◽  
Yuki Hama ◽  
Koichi Asamori ◽  
Takumi Ueda
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Data Processing ◽  
Time Series Data ◽  
Series Data ◽  
Short Time Fourier Transform ◽  
Numerical Errors ◽  
Spectral Transform ◽  
Frequency Components ◽  
Short Time

Abstract In the magnetotelluric (MT) method, the responses of the natural electromagnetic fields are evaluated by transforming time-series data into spectral data and calculating the apparent resistivity and phase. The continuous wavelet transform (CWT) can be an alternative to the short-time Fourier transform, and the applicability of CWT to MT data has been reported. There are, however, few cases of considering the effect of numerical errors derived from spectral transform on MT data processing. In general, it is desirable to adopt a window function narrow in the time domain for higher-frequency components and one in the frequency domain for lower-frequency components. In conducting the short-time Fourier transform, because the size of the window function is fixed unless the time-series data are decimated, there might be difference between the calculated MT responses and the true ones due to the numerical errors. Meanwhile, CWT can strike a balance between the resolution of the time and frequency domains by magnifying or reducing the wavelet, according to the value of frequency. Although the types of wavelet functions and their parameters influence the resolution of time and frequency, those calculation settings of CWT are often determined empirically. In this study, focusing on the frequency band between 0.001 Hz and 10 Hz, we demonstrated the superiority of utilizing CWT in MT data processing and determined its proper calculation settings in terms of restraining the numerical errors caused by the spectral transform of time-series data. The results obtained with the short-time Fourier transform accompanied with gradual decimation of the time-series data, called cascade decimation, were compared with those of CWT. The shape of the wavelet was changed by using different types of wavelet functions or their parameters, and the respective results of data processing were compared. Through these experiments, this study indicates that CWT with the complex Morlet function with its wavelet parameter k set to 6 ≤ k < 10 will be effective in restraining the numerical errors caused by the spectral transform.

scholarly journals Multiple Time Series Data Processing for Classification with Period Merging Algorithm

2014 ◽  
Vol 37 ◽  
pp. 301-308 ◽  
Author(s):  
Wei-Ti Su ◽  
Xiao-Ou Ping ◽  
Yi-Ju Tseng ◽  
Feipei Lai
Keyword(s):  
Time Series ◽  
Data Processing ◽  
Time Series Data ◽  
Series Data ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Merging Algorithm
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