Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Experimental data are often very complex since the underlying dynamical system may be unknown and the data may heavily be corrupted by noise. It is a crucial task to properly analyze data to get maximal information of the underlying dynamical system. This paper presents a novel principal component analysis (PCA) method based on symplectic geometry, called symplectic PCA (SPCA), to study nonlinear time series. Being nonlinear, it is different from the traditional PCA method based on linear singular value decomposition (SVD). It is thus perceived to be able to better represent nonlinear, especially chaotic data, than PCA. Using the chaotic Lorenz time series data, we show that this is indeed the case. Furthermore, we show that SPCA can conveniently reduce measurement noise.

Download Full-text

Moving dynamic principal component analysis for non-stationary multivariate time series

Computational Statistics ◽

10.1007/s00180-021-01081-8 ◽

2021 ◽

Cited By ~ 1

Author(s):

Fayed Alshammri ◽

Jiazhu Pan

Keyword(s):

Principal Component Analysis ◽

Time Series ◽

Time Series Data ◽

Multivariate Time Series ◽

Principal Component ◽

Real Data ◽

Component Analysis ◽

Series Data ◽

Dynamic Principal Component Analysis ◽

Dynamic Relationships

AbstractThis paper proposes an extension of principal component analysis to non-stationary multivariate time series data. A criterion for determining the number of final retained components is proposed. An advance correlation matrix is developed to evaluate dynamic relationships among the chosen components. The theoretical properties of the proposed method are given. Many simulation experiments show our approach performs well on both stationary and non-stationary data. Real data examples are also presented as illustrations. We develop four packages using the statistical software R that contain the needed functions to obtain and assess the results of the proposed method.

Download Full-text

Forecasting of UV-Vis absorbance time series using artificial neural networks combined with principal component analysis

Water Science & Technology ◽

10.2166/wst.2016.524 ◽

2016 ◽

Vol 75 (4) ◽

pp. 765-774

Author(s):

Leonardo Plazas-Nossa ◽

Thomas Hofer ◽

Günter Gruber ◽

Andres Torres

Keyword(s):

Neural Networks ◽

Principal Component Analysis ◽

Time Series ◽

Artificial Neural Networks ◽

Principal Component ◽

Component Analysis ◽

Series Data ◽

Visible Range ◽

Artificial Neural ◽

Uv Range

This work proposes a methodology for the forecasting of online water quality data provided by UV-Vis spectrometry. Therefore, a combination of principal component analysis (PCA) to reduce the dimensionality of a data set and artificial neural networks (ANNs) for forecasting purposes was used. The results obtained were compared with those obtained by using discrete Fourier transform (DFT). The proposed methodology was applied to four absorbance time series data sets composed by a total number of 5705 UV-Vis spectra. Absolute percentage errors obtained by applying the proposed PCA/ANN methodology vary between 10% and 13% for all four study sites. In general terms, the results obtained were hardly generalizable, as they appeared to be highly dependent on specific dynamics of the water system; however, some trends can be outlined. PCA/ANN methodology gives better results than PCA/DFT forecasting procedure by using a specific spectra range for the following conditions: (i) for Salitre wastewater treatment plant (WWTP) (first hour) and Graz West R05 (first 18 min), from the last part of UV range to all visible range; (ii) for Gibraltar pumping station (first 6 min) for all UV-Vis absorbance spectra; and (iii) for San Fernando WWTP (first 24 min) for all of UV range to middle part of visible range.

Download Full-text

A Time-Series Trend Forecast Method Based on Principal Component Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.472-475.2984 ◽

2012 ◽

Vol 472-475 ◽

pp. 2984-2987

Author(s):

Shu Di Wei ◽

Hui Huang Zhao

Keyword(s):

Principal Component Analysis ◽

Time Series ◽

Image Data ◽

Principal Component ◽

Component Analysis ◽

Series Data ◽

Text Data ◽

Related Research ◽

Time Series Method ◽

Scan Data

The time-series is the collection of chronological varying numerical ordered by time. It has a wide existence of image data, text data, hand-written data and the brain scan data patterns. The present research of time-series concentrates on series data transformation, similarity search, forecast, classification, clustering and Visualization etc. Furthermore the trend forecast of time-series is the major basis of other related research. This paper analyses the existing time-series forecasting methods and puts forward a new time-series method based on principal component analysis. The example tests the validity of the method of other related research.

Download Full-text

RESEARCH ON THE RELATIONSHIP BETWEEN INVESTOR SENTIMENT AND STOCK PRICE

10.12783/dtem/mebit2021/35642 ◽

2021 ◽

Author(s):

YI-MING DU ◽

RUI DING ◽

YI-LIN ZHANG ◽

TING ZHANG ◽

TAO ZHOU

Keyword(s):

Principal Component Analysis ◽

Behavioral Finance ◽

Stock Price ◽

Time Series Data ◽

Investor Sentiment ◽

Principal Component ◽

Component Analysis ◽

Series Data ◽

Macroeconomic Factors ◽

The Relationship

As one of the main contents of behavioral finance, investor sentiment has become a research hotspot in recent years. This paper takes the CSI300 index of China as the observation object, selects five emotional monthly time series data including lag one period from 2016 to 2020. The method of principal component analysis will be used to reduce the dimension of 10 groups of data. After eliminating the macroeconomic factors, the dimension reduction results are analyzed by the second principal component analysis to obtain the comprehensive index of emotion. Furthermore, a Vector Auto Regressive model (VAR) is established to investigate the relationship between ISIO and CSI300 of the stock market. The results show that investor sentiment and stock price interact with each other, but only in the short term. With more and more sufficient market information known, the effect is becoming insignificant.

Download Full-text

Assimilation of Principal Component Analysis and Wavelet with Kernel Support Vector Regression for Medium-Term Financial Time Series Forecasting

Regular issue - International Journal of Management and Humanities ◽

10.35940/ijmh.g0667.034720 ◽

2020 ◽

Vol 4 (7) ◽

pp. 40-48

Keyword(s):

Principal Component Analysis ◽

Time Series ◽

Support Vector Regression ◽

Financial Time Series ◽

Principal Component ◽

Component Analysis ◽

Time Series Forecasting ◽

Series Data ◽

Support Vector ◽

Financial Time

Entities and institutional financiers have gained a lot of growth from financial time series forecasting in recent times. But the major challenges of financial time series data are the high noise and complexity of its nature. Researchers in recent times have successfully engaged the application of support vector regression (SVR) to conquer this challenge. In this study principal component analysis (PCA) is applied to extract the low dimensionality and efficient feature information, while wavelet is used to pre-process the extracted features in other to nu1llify the influence of the noise in the features with a KSVR based forecasting model. The analysis is carried out based on the quarterly tax revenue data of 39 years from the first quarter of 1981 to the last quarter of 2016. The forecasting is made for ten quarters ahead. The initial empirical result shows that the multicollinearity has been reduced to zero (0), and the analytic result reveals that the proposed model PCA-W-KSVR outperforms KSVR, PCA-KSVR, and W-KSVR in terms of MAE, MAPE, MSE and RMSE

Download Full-text