The Time Domain and the Frequency Domain in Time Series Analysis

1968 ◽  
Vol 70 (1) ◽  
pp. 25 ◽  
Author(s):  
O. Brandes ◽  
J. Farley ◽  
M. Hinich ◽  
U. Zackrisson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Series Analysis ◽  
The Time Domain

A New Method of Time Series Analysis and its Application to Identifying Modal Parameters

1991 ◽  
Author(s):  
Yusheng He ◽  
Zhaoxiang Deng
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Domain ◽  
New Method ◽  
Modal Parameters ◽  
Frequency Domain Method ◽  
Identification Algorithm ◽  
Series Analysis ◽  
The Time Domain ◽  
Modeling Strategy

Abstract In the paper, the attention concentrates on the time domain modal analysis. A new method of time series analysis, which is formed mainly by an ideal modeling strategy and a new COR-IV method, is developed. In addition, an interesting parameter called as modal energy ratio, which is available for design reference, is defined and its identification algorithm is given. The new method presented in this paper and Frequency Domain Method (FDM) are performed on a frame of SG120 vehicle. It is shown by comparison between these two methods that the new method of time series analysis is practical.

Time series analysis of Holocene climate data

1990 ◽  
Vol 330 (1615) ◽  
pp. 601-616 ◽  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Domain ◽  
Small Sample ◽  
Climate Data ◽  
Holocene Climate ◽  
Series Analysis ◽  
Window Method ◽  
Band Limited ◽  
Periodic Components

Holocene climate records are imperfect proxies for processes containing complicated mixtures of periodic and random signals. I summarize time series analysis methods for such data with emphasis on the multiple-data-window technique. This method differs from conventional approaches to time series analysis in that a set of data tapers is applied to the data in the time domain before Fourier transforming. The tapers, or data windows, are discrete prolate spheroidal sequences characterized as being the most nearly band-limited functions possible among functions defined on a finite time domain. The multiple-window method is a small-sample theory and essentially an inverse method applied to the finite Fourier transform. For climate data it has the major advantage of providing a narrowband F -test for the presence and significance of periodic components and of being able to separate them from the non-deterministic part of the process. Confidence intervals for the estimated quantities are found by jackknifing across windows. Applied to 14 C records, this method confirms the presence of the ‘Suess wiggles’ and give an estimated period of 208.2 years. Analysis of the thickness variations of bristlecone pine growth rings shows a general absence of direct periodic components but a variation in the structure of the time series with a 2360-year period.

scholarly journals Interactive Education for Time-Domain Time Series Analysis using ITTSAE

2015 ◽  
Vol 48 (28) ◽  
pp. 751-756
Author(s):  
J.M. DÍaz ◽  
S. Dormido ◽  
D.E. Rivera
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Domain ◽  
Series Analysis ◽  
Interactive Education

scholarly journals Modeling the Gas price data Using Periodogram and Fourier Series Analysis: استخدام التحليل في مجال التردد لنمذجة بيانات مبيعات الغاز الطبيعي في الولايات المتحدة الأمريكية بواسطة شكل الدورة وتحليل سلسلة فورير

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Safia Abdullah Al Fadhel, Amal Al-Ser Al-khadir, Obeid Mahmo
Keyword(s):  
Fourier Series ◽  
Frequency Domain ◽  
Time Domain ◽  
Domain Analysis ◽  
The United States ◽  
Frequency Domain Method ◽  
Series Analysis ◽  
Frequency Domain Methods ◽  
The Time Domain ◽  
Fourier Series Analysis

:   This paper takes into account the application of the Periodogram and the Fourier Series Analysis. It is one of the non-parametric methods of Frequency domain analysis or spectral analysis of time series using Gas sales data in the United States of America from 1993-2014. In order to achieve these objectives، the data were obtained and then the Periodogram and the Fourier series methods were used to analyze the data. Based on the analysis، the cycle of variability within the period under study was 135 months، and a high Accuracy data model was estimated for the Fourier series which included trend، seasonal and error components. The RMSE، MASE and MAE standards were used to confirm the efficiency of the model and the model was then used to predict gas sales for six months، and we have 90% 95% confidence intervals for predictions. In addition، a time domain analysis was provided for the data series using Bok Jenkins method to obtain the appropriate ARMA model and to generate Predictions. Finally، a comparison was made between the accuracy measures of the time domain and frequency domain methods The frequency domain method competed with the time domain and the slight difference in efficiency.

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