scholarly journals Comparison of high versus low frequency cerebral physiology for cerebrovascular reactivity assessment in traumatic brain injury: a multi-center pilot study

2019 ◽  
Vol 34 (5) ◽  
pp. 971-994 ◽  
Author(s):  
Eric P. Thelin ◽  
Rahul Raj ◽  
Bo-Michael Bellander ◽  
David Nelson ◽  
Anna Piippo-Karjalainen ◽  
...  
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Moving Average ◽  
Cerebrovascular Reactivity ◽  
Reactivity Index ◽  
Mean Values ◽  
Similar Information ◽  
Statistical Time ◽  
Map Data ◽  
Over Time

Abstract Current accepted cerebrovascular reactivity indices suffer from the need of high frequency data capture and export for post-acquisition processing. The role for minute-by-minute data in cerebrovascular reactivity monitoring remains uncertain. The goal was to explore the statistical time-series relationships between intra-cranial pressure (ICP), mean arterial pressure (MAP) and pressure reactivity index (PRx) using both 10-s and minute data update frequency in TBI. Prospective data from 31 patients from 3 centers with moderate/severe TBI and high-frequency archived physiology were reviewed. Both 10-s by 10-s and minute-by-minute mean values were derived for ICP and MAP for each patient. Similarly, PRx was derived using 30 consecutive 10-s data points, updated every minute. While long-PRx (L-PRx) was derived via similar methodology using minute-by-minute data, with L-PRx derived using various window lengths (5, 10, 20, 30, 40, and 60 min; denoted L-PRx_5, etc.). Time-series autoregressive integrative moving average (ARIMA) and vector autoregressive integrative moving average (VARIMA) models were created to analyze the relationship of these parameters over time. ARIMA modelling, Granger causality testing and VARIMA impulse response function (IRF) plotting demonstrated that similar information is carried in minute mean ICP and MAP data, compared to 10-s mean slow-wave ICP and MAP data. Shorter window L-PRx variants, such as L-PRx_5, appear to have a similar ARIMA structure, have a linear association with PRx and display moderate-to-strong correlations (r ~ 0.700, p < 0.0001 for each patient). Thus, these particular L-PRx variants appear closest in nature to standard PRx. ICP and MAP derived via 10-s or minute based averaging display similar statistical time-series structure and co-variance patterns. PRx and L-PRx based on shorter windows also behave similarly over time. These results imply certain L-PRx variants may carry similar information to PRx in TBI.

scholarly journals Beauty and Ugliness of Aggregation over Time: A Survey

2017 ◽  
Vol 68 (3) ◽  
Author(s):  
Nlandu Mamingi
Keyword(s):  
Public Policy ◽  
Time Series ◽  
High Frequency ◽  
Low Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Economic Time Series ◽  
Negative Effects ◽  
Economic Time ◽  
Over Time

AbstractThis paper delivers an up-to-date literature review dealing with aggregation over time of economic time series, e.g. the transformation of high-frequency data to low frequency data, with a focus on its benefits (the beauty) and its costs (the ugliness). While there are some benefits associated with aggregating data over time, the negative effects are numerous. Aggregation over time is shown to have implications for inferences, public policy and forecasting.

scholarly journals Frequency-specific suppression of locomotor components by the white+ gene in Drosophila melanogaster adult flies

2020 ◽  
Author(s):  
Chengfeng Xiao ◽  
Shuang Qiu
Keyword(s):  
Time Series ◽  
Drosophila Melanogaster ◽  
High Frequency ◽  
Copy Number ◽  
Path Length ◽  
High Frequencies ◽  
Eye Color ◽  
Specific Suppression ◽  
Over Time ◽  
Color Gene

AbstractThe classic eye-color gene white+ (w+) in Drosophila melanogaster (fruitfly) has unexpected behavioral consequences. How w+ affect locomotion of adult flies is largely unknown. Here, we show that w+ selectively suppresses locomotor components at relatively high frequencies (> 0.1 Hz). The wildtype Canton-S male flies walked intermittently in circular arenas while the white-eyed w1118 flies walked continuously. Through careful control of genetic and cytoplasmic backgrounds, we found that w+ was associated with intermittent walking. w+-carrying male flies had smaller median values of path length per second (PPS) and reduced 5-min path length compared with w1118-carrying males. Additionally, flies carrying 2-4 genomic copies of mini-white+ (mw+) showed reduced median PPSs and decreased 5-min path length compared with w1118 flies, and the suppression was dependent on the copy number of mw+. Fourier transform of the time series (i.e. PPSs over time) indicated that w+/mw+ specifically suppressed the locomotor components at relatively high frequencies (> 0.1 Hz). Lastly, the downregulation of w+ in neurons but not glial cells resulted in an increased percentage of high-frequency locomotor components. We concluded that w+ suppressed the locomotion of adult flies by selectively reducing the high-frequency locomotor components.

scholarly journals A wavelet-SARIMA-ANN hybrid model for precipitation forecasting

2016 ◽  
Vol 28 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Maryam Shafaei ◽  
Jan Adamowski ◽  
Ahmad Fakheri-Fard ◽  
Yagob Dinpashoh ◽  
Kazimierz Adamowski
Keyword(s):  
Time Series ◽  
High Frequency ◽  
Moving Average ◽  
Low Frequency ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Monthly Precipitation ◽  
Ann Model ◽  
Precipitation Time Series ◽  
Precipitation Forecasting

Abstract Given its importance in water resources management, particularly in terms of minimizing flood or drought hazards, precipitation forecasting has seen a wide variety of approaches tested. As monthly precipitation time series have nonlinear features and multiple time scales, wavelet, seasonal auto regressive integrated moving average (SARIMA) and hybrid artificial neural network (ANN) methods were tested for their ability to accurately predict monthly precipitation. A 40-year (1970–2009) precipitation time series from Iran’s Nahavand meteorological station (34°12’N lat., 48°22’E long.) was decomposed into one low frequency subseries and several high frequency sub-series by wavelet transform. The low frequency sub-series were predicted with a SARIMA model, while high frequency subseries were predicted with an ANN. Finally, the predicted subseries were reconstructed to predict the precipitation of future single months. Comparing model-generated values with observed data, the wavelet-SARIMA-ANN model was seen to outperform wavelet-ANN and wavelet-SARIMA models in terms of precipitation forecasting accuracy.

scholarly journals Decreasing stochasticity through enhanced seasonality in measles epidemics

2009 ◽  
Vol 7 (46) ◽  
pp. 727-739 ◽  
Author(s):  
N. B. Mantilla-Beniers ◽  
O. N. Bjørnstad ◽  
B. T. Grenfell ◽  
P. Rohani
Keyword(s):  
Time Series ◽  
Population Dynamics ◽  
Population Cycles ◽  
Theoretical Work ◽  
Mortality Data ◽  
Complex Population ◽  
Epidemiological Transitions ◽  
Statistical Time ◽  
Time Changes ◽  
Over Time

Seasonal changes in the environment are known to be important drivers of population dynamics, giving rise to sustained population cycles. However, it is often difficult to measure the strength and shape of seasonal forces affecting populations. In recent years, statistical time-series methods have been applied to the incidence records of childhood infectious diseases in an attempt to estimate seasonal variation in transmission rates, as driven by the pattern of school terms. In turn, school-term forcing was used to show how susceptible influx rates affect the interepidemic period. In this paper, we document the response of measles dynamics to distinct shifts in the parameter regime using previously unexplored records of measles mortality from the early decades of the twentieth century. We describe temporal patterns of measles epidemics using spectral analysis techniques, and point out a marked decrease in birth rates over time. Changes in host demography alone do not, however, suffice to explain epidemiological transitions. By fitting the time-series susceptible–infected–recovered model to measles mortality data, we obtain estimates of seasonal transmission in different eras, and find that seasonality increased over time. This analysis supports theoretical work linking complex population dynamics and the balance between stochastic and deterministic forces as determined by the strength of seasonality.

A threshold mixed count time series model: estimation and application

2019 ◽  
Vol 24 (2) ◽  
Author(s):  
Mardi Dungey ◽  
Vance L. Martin ◽  
Chrismin Tang ◽  
Andrew Tremayne
Keyword(s):  
Time Series ◽  
Global Financial Crisis ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Efficient Method Of Moments ◽  
The Great Depression ◽  
Autoregressive Moving Average Model ◽  
Global Banking ◽  
Method Of Moments Estimator ◽  
Over Time

AbstractA new class of integer time series models is proposed to capture the dynamic transmission of count processes over time. The approach extends existing integer mixed autoregressive-moving average models (INARMA) by allowing for shifts in the dynamics of the count process through regime changes, referred to as a threshold integer autoregressive-moving average model (TINARMA). An efficient method of moments estimator is proposed, with standard errors based on subsampling, as maximum likelihood methods are infeasible for TINARMA processes. Applying the framework to global banking crises over 200 years of data, the empirical results show strong evidence of autoregressive and moving average dynamics which vary across systemic and nonsystemic regimes over time. Coherent forecast distributions are also produced with special attention given to the Great Depression and the more recent Global Financial Crisis.

scholarly journals Trends, variations and prediction of staff sickness absence rates among NHS ambulance services in England: a time series study

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e053885
Author(s):  
Zahid B Asghar ◽  
Paresh Wankhade ◽  
Fiona Bell ◽  
Kristy Sanderson ◽  
Kelly Hird ◽  
...  
Keyword(s):  
Time Series ◽  
Sickness Absence ◽  
Moving Average ◽  
Clinical Staff ◽  
Full Time ◽  
Full Time Equivalent ◽  
Service Staff ◽  
Individual Participant ◽  
Enabling Services ◽  
Over Time

ObjectivesOur aim was to measure ambulance sickness absence rates over time, comparing ambulance services and investigate the predictability of rates for future forecasting.SettingAll English ambulance services, UK.DesignWe used a time series design analysing published monthly National Health Service staff sickness rates by gender, age, job role and region, comparing the 10 regional ambulance services in England between 2009 and 2018. Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) models were developed using Stata V.14.2 and trends displayed graphically.ParticipantsIndividual participant data were not available. The total number of full-time equivalent (FTE) days lost due to sickness absence (including non-working days) and total number of days available for work for each staff group and level were available. In line with The Data Protection Act, if the organisation had less than 330 FTE days available during the study period it was censored for analysis.ResultsA total of 1117 months of sickness absence rate data for all English ambulance services were included in the analysis. We found considerable variation in annual sickness absence rates between ambulance services and over the 10-year duration of the study in England. Across all the ambulance services the median days available were 1 336 888 with IQR of 548 796 and 73 346 median days lost due to sickness absence, with IQR of 30 551 days. Among clinical staff sickness absence varied seasonally with peaks in winter and falls over summer. The winter increases in sickness absence were largely predictable using seasonally adjusted (SARIMA) time series models.ConclusionSickness rates for clinical staff were found to vary considerably over time and by ambulance trust. Statistical models had sufficient predictive capability to help forecast sickness absence, enabling services to plan human resources more effectively at times of increased demand.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

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