Synthesis of natural electric and magnetic Time-series using Inter-station transfer functions and time-series from a Neighboring site (STIN): Applications for processing MT data

2017 ◽  
Vol 122 (8) ◽  
pp. 5835-5851 ◽  
Author(s):  
Hui Wang ◽  
Joan Campanyà ◽  
Jiulong Cheng ◽  
Guowei Zhu ◽  
Wenbo Wei ◽  
...  
Keyword(s):  
Time Series ◽  
Transfer Functions ◽  
Neighboring Site

scholarly journals Analysis of the time series of waste water quality at the inflow of the wastewater treatment plant and transfer functions

2014 ◽  
Vol 62 (1) ◽  
pp. 55-59 ◽  
Author(s):  
Ivan Nesmerak ◽  
Sarka D. Blazkova
Keyword(s):  
Time Series ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Transfer Functions ◽  
Treatment Plant ◽  
Total Precipitation ◽  
Periodic Components ◽  
The Relationship ◽  
Pollution Loads ◽  
Daily Total

Abstract Time series of the daily total precipitation, daily wastewater discharges and daily concentrations and pollution loads of BOD5, COD, SS, N-NH4, Ntot and Ptot were analyzed at the inflow to the wastewater treatment plant (WWTP) for a larger city in 2004-2009 (WWTP is loaded by pollution from 435,000 equivalent inhabitants). The time series of the outflow from a WWTP was also available for 2007. The time series of daily total precipitation, daily wastewater discharges, concentrations and pollution loads at the inflow and outflow from the WWTP were standardized year by year to exclude a long-term trend, and periodic components with a period of 7 days and 365 days (and potentially also 186.5 days) were excluded from the standardized series. However, these two operations eliminated only a small part of the variance; there was a substantial reduction in the variance only for ammonium nitrogen and total nitrogen at the inflow and outflow from a WWTP. The relationship between the inflow into a WWTP and the outflow from a WWTP for the concentrations and pollution loads was described by simple transfer functions (SISO models) and more complicated transfer functions (MISO models). A simple transfer function (SISO model) was employed to describe the relationship between the daily total precipitation and the wastewater discharge.

scholarly journals Effect of tobacco control policies on the Swedish smoking quitline using intervention time-series analysis

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033650
Author(s):  
Xingwu Zhou ◽  
Alessio Crippa ◽  
Anna-Karin Danielsson ◽  
Maria R Galanti ◽  
Nicola Orsini
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Passive Smoking ◽  
Transfer Functions ◽  
Positive Impact ◽  
Calendar Time ◽  
Series Analysis ◽  
Phone Calls ◽  
Pictorial Warnings ◽  
Tobacco Tax

ObjectivesTo coherently examine the responsiveness of the Swedish National Tobacco Quitline (SNTQ) to different types of anti-smoking policies over an extended period of calendar time.DesignQuasi-experimental design with an intervention time-series analysis based on 19 years series of data collected between January 1999 and August 2017 (224 months). Statistical inference on calling rates and rate ratios was obtained using intervention time-series models (Poisson regression and transfer functions).ParticipantsA total of 179 851 phone calls to the SNTQ.InterventionsRecent application of the 2014/40/ European Union (EU) Tobacco Products Directive in 2016. Historical interventions such as a campaign on passive smoking in January 2001; introduction of larger text warnings on cigarette packages since September 2002; banning smoking in restaurants since June 2005; and tobacco tax increase by 10% since January 2012.Outcome measureCalling rates to the SNTQ expressed per 100 000 smokers.SettingSweden.ResultsThe introduction of large pictorial warnings together with text warnings on cigarette packages (May 2016) was associated with a 35% increase in SNTQ calling rate (95% CI 1.16 to 1.57). The campaign on passive smoking (Jan 2001) was associated with a 61% higher calling rate (95% CI 1.06 to 2.45). Larger text warnings on cigarette packs (Sept 2002) conferred a 28% increment in the calling rate (95% CI 1.15 to 1.42); and prohibition to smoke in restaurants (Jun 2005) was associated with a 15% increase in the calling rate (95% CI 1.01 to 1.30). The 10% tobacco tax increase (Jan 2012) was associated with a 3% higher calling rate (95% CI 0.90 to 1.19).ConclusionsWithin an overall decreasing trend of daily smoking in Sweden, we found that the recent introduction of pictorial warnings together with text warnings and referral text had a discernible positive impact on the calling rates to the smoking quitline. We were also able to detect a likely impact of earlier nationwide interventions.

scholarly journals Heterogeneous transfer functions multi-layer perceptron (MLP) for meteorological time series forecasting

2015 ◽  
Vol 06 (02) ◽  
pp. 1550013
Author(s):  
C. Voyant ◽  
M. L. Nivet ◽  
C. Paoli ◽  
M. Muselli ◽  
G. Notton
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Transfer Functions ◽  
Meteorological Data ◽  
Multi Layer Perceptron ◽  
Time Index ◽  
Seasonal Time Series ◽  
Hidden Layer ◽  
Seasonal Aspect ◽  
Data Estimation

In this paper, we propose to study four meteorological and seasonal time series (TS) coupled with a multi-layer perceptron (MLP) modeling. We chose to combine two transfer functions for the nodes of the hidden layer, and to use a temporal indicator (time index as input) in order to take into account the seasonal aspect of the studied TS. The results of the prediction concern two years of measurements and the learning step, eight independent years. We show that this methodology can improve the accuracy of meteorological data estimation compared to a classical MLP modeling with a homogenous transfer function.

The Intervention Component: X( It)

2019 ◽  
pp. 98-135
Author(s):  
David McDowall ◽  
Richard McCleary ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Transfer Functions ◽  
Intervention Model ◽  
Intervention Component ◽  
Noise Component ◽  
Survey Knowledge ◽  
Short Course ◽  
Modeling Strategy ◽  
The Impact

Chapter 4 introduces the full ARIMA intervention model. Most substantive theories specify the intervention as an exogenous dichotomy. A Box-Tiao transfer function then distributes the intervention's response across the endogenous time series to reflect a theoretically specified onset and duration. Transfer functions allow the noise component to be parsed from the residualized time series. Theoretical specification of the intervention model requires at least some sense of the onset and duration of the impact. Detailed analyses of ten time series demonstrate how to handle interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts. One popular version of an ITSA short course ends with Chapter 4. Although statistically adequate ARIMA models can be built using the modeling strategy described in Chapters 3-4, survey knowledge of the auxiliary methods described in Chapter 5 is recommended.

Learning recurrent transfer functions from data: From climate variability to high river discharge

2020 ◽  
Author(s):  
Mateusz Norel ◽  
Krzysztof Krawiec ◽  
Zbigniew Kundzewicz
Keyword(s):  
Time Series ◽  
River Discharge ◽  
Short Term Memory ◽  
Transfer Functions ◽  
Southern Oscillation ◽  
Flood Hazard ◽  
Atlantic Multidecadal Oscillation ◽  
Statistical Characteristics ◽  
Learning Tools ◽  
Discharge Data

<p>Interpretation of flood hazard and its variability remains a major challenge for climatologists, hydrologists and water management experts. This study investigates the existence of links between variability in high river discharge, worldwide, and inter-annual and inter-decadal climate oscillation indices: El Niño-Southern Oscillation, North Atlantic Oscillation, Pacific Interdecadal Oscillation, and Atlantic Multidecadal Oscillation. Global river discharge data used here stem from the ERA-20CM-R reconstruction at 0.5 degrees resolution and form a multidimensional time series, with each observation being a spatial matrix of estimated discharge volume. Elements of matrices aligned spatially form time series which were used to induce dedicated predictive models using machine learning tools, including multivariate regression (e.g. ARMA) and recurrent neural networks (RNNs), in particular the Long Short Term Memory model (LSTM) that proved to be effective in many other application areas. The models are thoroughly tested and juxtaposed in hindcasting mode on a separate test set and scrutinized with respect to their statistical characteristics. We hope to be able to contribute to improvement of interpretation of variability of flood hazard and reduction of uncertainty.</p>

scholarly journals Transfer functions: learning about a lagged exposure-outcome association in time-series data

2021 ◽  
Author(s):  
Hiroshi Mamiya ◽  
Alexandra M. Schmidt ◽  
Erica E. M. Moodie ◽  
David L. Buckeridge
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Transfer Functions ◽  
Environmental Research ◽  
Series Data ◽  
Time Lags ◽  
Food Purchasing ◽  
Lag Model ◽  
Time Lagged ◽  
Sugary Beverages

AbstractMany population exposures in time-series analysis, including food marketing, exhibit a time-lagged association with population health outcomes such as food purchasing. A common approach to measuring patterns of associations over different time lags relies on a finite-lag model, which requires correct specification of the maximum duration over which the lagged association extends. However, the maximum lag is frequently unknown due to the lack of substantive knowledge or the geographic variation of lag length. We describe a time-series analytical approach based on an infinite lag specification under a transfer function model that avoids the specification of an arbitrary maximum lag length. We demonstrate its application to estimate the lagged exposure-outcome association in food environmental research: display promotion of sugary beverages with lagged sales.

Sign in / Sign up

Export Citation Format

Share Document