scholarly journals Real Time Prediction of Ship Response to Ocean Waves Using Time Series Analysis

1981 ◽  
Author(s):  
I. Yumori
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Real Time ◽  
Ocean Waves ◽  
Time Prediction ◽  
Series Analysis

Research on Ship Swaying Motion Prediction Based on Multi-Variable Chaotic Time Series Analysis

2013 ◽  
Vol 712-715 ◽  
pp. 1550-1554
Author(s):  
Xin Dong Yang ◽  
Zuo Chao Wang ◽  
Ai Guo Shi ◽  
Bo Liu ◽  
Li Li
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Chaotic Time Series ◽  
Ship Motion ◽  
Ship Motions ◽  
Sea State ◽  
Time Prediction ◽  
Series Analysis ◽  
Short Time ◽  
Chaotic Time Series Analysis

Wind and waves have particularly significant influence upon exertion of naval vessels battle effectiveness. It is urgently necessary to improve the ability of the Navy to carry out combat service in severe sea state normally. This paper aims to obtain the accurate prediction of ship motions with second level predictable time in real waves. According to the characteristics of the ship motion, the research on extremely short-time prediction of ship motion has been carried out based on multi-variable chaotic time series analysis, and the effectiveness of the prediction of ship motion in real wave is highly improved.

scholarly journals A Time Series Analysis - Based Forecasting Approach for the Indian Realty Sector

2021 ◽  
Author(s):  
Jaydip Sen
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Real Time ◽  
Stock Prices ◽  
Time Series Data ◽  
Rapid Development ◽  
Series Data ◽  
Time Processing ◽  
Forecasting Models ◽  
Series Analysis

<p>Prediction of stock prices using time series analysis is quite a difficult and challenging task since the stock prices usually depict random patterns of movement. However, the last decade has witnessed rapid development and evolution of sophisticated algorithms for complex statistical analysis. These algorithms are capable of processing a large volume of time series data executing on high-performance hardware and parallel computing architecture. Thus computations which were seemingly impossible to perform a few years back are quite amenable to real-time time processing and effective analysis today. Stock market time series data are large in volume, and quite often need real-time processing and analysis. Thus it is quite natural that research community has focused on designing and developing robust predictive models for accurately forecasting stochastic nature of stock price movements. This work presents a time series decomposition-based approach for understanding the past behavior of the realty sector of India, and forecasting its behavior in future. While the forecasting models are built using the time series data of the realty sector for the period January 2010 till December 2015, the prediction is made for the time series index values for the months of the year 2016. A detailed comparative analysis of the methods are presented with respect to their forecasting accuracy and extensive results are provided to demonstrate the effectiveness of the six proposed forecasting models. </p>

scholarly journals Peramalan Data IHSG Menggunakan Fuzzy Time Series

2013 ◽  
Vol 7 (1) ◽  
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Time Series Analysis ◽  
Real Time ◽  
Soft Computing ◽  
Time Series Data ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Time Data ◽  
Series Analysis

AbstrakFuzzy time series merupakan salah satu metode soft computing yang telah digunakan dan diterapkan dalam analisis data runtun waktu. Tujuan utama dari fuzzy time series adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas pada sembarang data real time, termasuk data pasar modal.Banyak peneliti yang telah berkontribusi dalam pengembangan analisis data runtun waktu menggunakan fuzzy time series, seperti Chen dan Hsu [1], Jilani dkk. [2], serta Stevenson dan Porter [3]. Dalam penelitian ini, dicoba untuk menerapkan metode fuzzy time series pada salah satu indikator pergerakan harga saham, yakni data IHSG (Indeks Harga Saham Gabungan).Kinerja metode yang diusulkan dievaluasi dengan menghitung tingkat akurasi dan tingkat kehandalan metode fuzzy time series yang diterapkan pada data IHSG. Melalui pendekatan ini, diharapkan metode fuzzy time series dapat menjadi alternatif untuk memprediksi data IHSG yang merupakan salah satu indikator pergerakan harga saham di Indonesia. Kata kunci – fuzzy time series, data runtun waktu, soft computing, IHSG AbstractFuzzy time series is one of the soft computing method that been used and implemented in time series analysis. The main goal of fuzzy time series is to predict time series data that can be used widely in any real time data, including stock market share.Many researchers have contributed in the development of fuzzy time series analysis, such as Chen and Hsu [1], Jilani [2], and Stevenson and Porter [3]. In this research, we will try to implement the fuzzy time series method in one of the stock market change indicator, i.e. the Jakarta composite index or also known as IHSG (Indeks Harga Saham Gabungan).The research is continued by calculating the accuracy and robustness of the method which has been implemented on IHSG data. By this approach, we hope it can be an alternative to predict the IHSG data which is an indicator of stock price changes in Indonesia. Keywords – fuzzy time series, time series data, soft computing, IHSG

Some Preliminary Results of an Analysis of Coordinates Time Series of RTK Network LitPos

2017 ◽  
Author(s):  
Karolis Galinauskas ◽  
Rimvydas Baniulis ◽  
Eimuntas Paršeliunas ◽  
Marius Petniunas
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Real Time ◽  
Positioning System ◽  
Post Processing ◽  
Coordinate Time ◽  
Preliminary Results ◽  
Coordinate Time Series ◽  
Series Analysis ◽  
Analysis Of Time Series

LitPOS (Lithuanian Positioning System), the network of permanent reference GNSS stations, became operational in July 2007. It provides data both for real-time and post-processing applications. Coordinate time series were obtained from LitPOS reprocessing data for years 2008–2014. The reprocessing was done using Bernese 5.2 software. The analysis of time series was done with Matlab routine “GITSA” (GPS Interactive Time Series Analysis). In this paper the review of obtained coordinate time series will be given, in addition error tracking and some of the results will be assessed. An analysis of discontinuities and outliers with possible problem solutions will be provided. As a result more consistent accumulated multi-year solution is presented.

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