scholarly journals Length of Time-Series Gait Data on Lyapunov Exponent for Fall Risk Detection

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Victoria Smith Hussain ◽  
Christopher W. Frames ◽  
Thurmon E. Lockhart
Keyword(s):  
Time Series ◽  
Fall Risk ◽  
Time Series Data ◽  
Fixed Number ◽  
Community Dwelling ◽  
Series Data ◽  
Data Points ◽  
Measurement Units ◽  
Lower Lumbar ◽  
Dynamics Stability

Falls are the leading cause of disability in older adults with a third of adults over the age of 65 falling every year. Quantitative fall risk assessments using inertial measurement units and local dynamics stability (LDS) have shown that it is possible to identify at-risk persons. However, there are inconsistencies in the literature on how to calculate LDS and how much data is required for a reliable result. This study investigates the reliability and minimum required strides for 6 algorithm-normalization method combinations when computing LDS using young healthy and community dwelling elderly individuals. Participants wore an accelerometer at the lower lumbar while they walked for three minutes up and down a long hallway. This study concluded that the Rosenstein et al. algorithm was successfully and reliably able to differentiate between both populations using only 50 strides. It was also found normalizing the gait time series data by either truncating the data using a fixed number of strides or using a fixed number of strides and normalizing the entire time series to a fixed number of data points performed better when using the Rosenstein et al. algorithm.

An Analysis of Coincidence between KSE-100 and S&P 500 Index using Spectral Approach

2015 ◽  
Vol 4 (2) ◽  
Author(s):  
Syed Monis Jawed
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
High Frequency ◽  
Time Series Data ◽  
Daily Basis ◽  
Series Data ◽  
Spectral Approach ◽  
In Series ◽  
Data Points

<span>When dealing with time series data, particularly of higher frequency,<br /><span>we are often interested in figuring out periods which are of vital<br /><span>importance. Here in this research, the returns on KSE-100 and S&amp;P<br /><span>500 index are taken on daily basis from September 2001 to June 2013.<br /><span>As thousands of data points (due to high frequency) are considered,<br /><span>it is impossible for us to figure out any pattern in series, unless<br /><span>suitable filtering is applied on them. For this purpose, a power<br /><span>spectrum will be made by means of a fast fourier transform. This will<br /><span>yield us the events that has influenced KSE-100 index considerably<br /><span>in post 9/11 scenario.</span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span>

DETECTING NONLINEAR DETERMINISM IN VOICED SOUNDS OF JAPANESE VOWEL /a/

2000 ◽  
Vol 10 (08) ◽  
pp. 1973-1979 ◽  
Author(s):  
TAKAYA MIYANO ◽  
AKIRA NAGAMI ◽  
ISAO TOKUDA ◽  
KAZUYUKI AIHARA
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Time Series Analysis ◽  
Basis Function ◽  
Speech Signal ◽  
Time Series Data ◽  
Series Data ◽  
Radial Basis Function Networks ◽  
Data Points ◽  
Surrogate Method

Nonlinear determinism in voiced sounds of the Japanese vowel /a/ is tested by the time series analysis associated with the surrogate method. To capture nonlinear dynamics underlying the speech signal, we apply the generalized radial basis function networks as nonlinear predictors to the time series data. The optimized network parameters may show a trail of the nonlinear dynamics though not conspicuously. This may be due to paucity of data points.

scholarly journals Application of ARIMA Model for River Discharges Analysis

2017 ◽  
Vol 4 (1) ◽  
pp. 27 ◽  
Author(s):  
Bhola NS Ghimire
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Series Data ◽  
Hydrological Data ◽  
Time Component ◽  
Data Points ◽  
Auto Regressive ◽  
Component Time

<p class="Default">Time series data often arise when monitoring hydrological processes. Most of the hydrological data are time related and directly or indirectly their analysis related with time component. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for. Many methods and approaches for formulating time series forecasting models are available in literature. This study will give a brief overview of auto-regressive integrated moving average (ARIMA) process and its application to forecast the river discharges for a river. The developed ARIMA model is tested successfully for two hydrological stations for a river in US.</p><p><strong>Journal of Nepal Physical Society</strong><em><br /></em>Volume 4, Issue 1, February 2017, Page: 27-32</p>

Study of Cubic Splines and Fourier Series as Interpolation Techniques for Filling in Short Periods of Missing Building Energy Use and Weather Data

2005 ◽  
Vol 128 (2) ◽  
pp. 226-230 ◽  
Author(s):  
Juan-Carlos Baltazar ◽  
David E. Claridge
Keyword(s):  
Time Series ◽  
Fourier Series ◽  
Energy Use ◽  
Time Series Data ◽  
Linear Interpolation ◽  
Cubic Splines ◽  
Weather Data ◽  
Series Data ◽  
Data Sets ◽  
Data Points

A study of cubic splines and Fourier series as interpolation techniques for filling in missing hourly data in energy and meteorological time series data sets is presented. The procedure developed in this paper is based on the local patterns of the data around the gaps. Artificial gaps, or “pseudogaps,” created by deleting consecutive data points from the measured data sets, were filled using four variants of the cubic spline technique and 12 variants of the Fourier series technique. The accuracy of these techniques was compared to the accuracy of results obtained using linear interpolation to fill the same pseudogaps. The pseudogaps filled were 1–6 data points in length created in 18 year-long sets of hourly energy use and weather data. More than 1000 pseudogaps of each gap length were created in each of the 18 data sets and filled using each of the 17 techniques evaluated. Use of mean bias error as the selection criterion found that linear interpolation is superior to the cubic spline and Fourier series methodologies for filling gaps of dry bulb and dew point temperature time series data. For hourly building cooling and heating use data, the Fourier series approach with 24 data points before and after each gap and six terms was found to be the most suitable; where there are insufficient data points to apply this approach, simple linear interpolation is recommended.

Feature-Based Online Representation Algorithm for Streaming Time Series Similarity Search

2019 ◽  
Vol 34 (05) ◽  
pp. 2050010 ◽  
Author(s):  
Peng Zhan ◽  
Changchang Sun ◽  
Yupeng Hu ◽  
Wei Luo ◽  
Jiecai Zheng ◽  
...  
Keyword(s):  
Time Series ◽  
Big Data ◽  
Similarity Search ◽  
High Speed ◽  
High Performance ◽  
Time Series Data ◽  
Rapid Development ◽  
Feature Representation ◽  
Series Data ◽  
Data Points

With the rapid development of information technology, we have already access to the era of big data. Time series is a sequence of data points associated with numerical values and successive timestamps. Time series not only has the traditional big data features, but also can be continuously generated in a high speed. Therefore, it is very time- and resource-consuming to directly apply the traditional time series similarity search methods on the raw time series data. In this paper, we propose a novel online segmenting algorithm for streaming time series, which has a relatively high performance on feature representation and similarity search. Extensive experimental results on different typical time series datasets have demonstrated the superiority of our method.

Fallability in the Visual Assessment of Behavioural Interventions: Time-Series Statistics to Analyse Time-Series Data

1986 ◽  
Vol 3 (1) ◽  
pp. 26-33 ◽  
Author(s):  
Christopher Sharpley
Keyword(s):  
Time Series ◽  
Visual Analysis ◽  
Time Series Data ◽  
Model Identification ◽  
Interrupted Time Series ◽  
Visual Assessment ◽  
Series Data ◽  
Behavioural Interventions ◽  
Data Points ◽  
Clinical Conditions

The use of visual analysis alone to determine the presence of significant and generalizable effects in typical behavioural interventions is subject to a series of possible errors which result in high levels of unreliability when data are analysed in this way. The presence of autocorrelation in most behavioural data poses a serious threat to visual and traditional analysis of such data, a threat which can be avoided by use of the more appropriate interrupted time-series (TMS) statistics. Although previously suggested as reasons for not using TMS procedures, the issues of model-identification and number of data points required for TMS are discussed and shown to be invalid arguments against the use of TMS. A case is made for visual analysis of behavioural data as an appropriate procedure only under certain constrained clinical conditions.

International interdependence: Some long-term trends and recent changes

1975 ◽  
Vol 29 (4) ◽  
pp. 1021-1034 ◽  
Author(s):  
Peter J. Katzenstein
Keyword(s):  
Twentieth Century ◽  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Empirical Measures ◽  
Long Term Changes ◽  
Data Points ◽  
Long Term Trends

This paper provides data on long-term changes in international interdependence. Transactions exchanged between societies and states are one possible means of interdependence. Two decades ago Karl Deutsch collected a substantial amount of data which illustrated a long-term decline in transactions since the beginning of the twentieth century. An extension of his time series data points to a possibly important reversal of that trend in recent years. This conclusion is in agreement with other empirical measures of international interdependence.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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