scholarly journals Multifractal detrended moving average analysis of Kepler stars with surface differential rotation traces

2019 ◽  
Vol 488 (3) ◽  
pp. 3274-3297 ◽  
Author(s):  
D B de Freitas ◽  
M M F Nepomuceno ◽  
J G Cordeiro ◽  
M L Das Chagas ◽  
J R De Medeiros
Keyword(s):  
Time Series ◽  
Differential Rotation ◽  
Time Series Data ◽  
Moving Average ◽  
Rotation Period ◽  
Series Data ◽  
Long Term Memory ◽  
The Sun ◽  
Average Analysis

ABSTRACT A multifractal formalism is employed to analyse high-precision time-series data of Kepler stars with surface differential rotation (DR) traces. The multifractal detrended moving average (MFDMA) algorithm has been explored to characterize the multiscale behaviour of the observed time series from a sample of 662 stars selected with parameters close to those of the Sun (e.g. effective temperature, mass, effective gravity and rotation period). Among these stars, 141 have surface DR traces, whereas 521 have no detected DR signatures. In our sample, we also include the Sun in its active phase. Our results can be summarized in two points. First, our work suggests that star-spots for time series with and without DR have distinct dynamics. Secondly, the magnetic fields of active stars are apparently governed by two mechanisms with different levels of complexity for fluctuations. Throughout the course of the study, we identified an overall trend whereby the DR is distributed in two H regimes segregated by the degree of asymmetry A, where H-index denotes the global Hurst exponent that is used as a measure of long-term memory of time series. As a result, we show that the degree of asymmetry can be considered a segregation factor that distinguishes the DR behaviour when related to the effect of the rotational modulation on the time series. In summary, the multifractality signals in our sample are the result of magnetic activity control mechanisms leading to activity-related long-term persistent signatures.

scholarly journals PERAMALAN JUMLAH PENUMPANG KERETA API MENGGUNAKAN METODE ARIMA, INTERVENSI DAN ARFIMA (Studi Kasus : Penumpang Kereta Api Kelas Lokal EkonomiDAOP IV Semarang)

2018 ◽  
Vol 7 (1) ◽  
pp. 96-109
Author(s):  
Helmi Panjaitan ◽  
Alan Prahutama ◽  
Sudarno Sudarno
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Time Series Data ◽  
Moving Average ◽  
Step Function ◽  
Local Economy ◽  
Stationary Time Series ◽  
Series Data ◽  
Short Term

Autoregressive Integrated Moving Average (ARIMA) is stationary time series model after differentiation. Differentiation value of ARIMA method is an integer so it is only able to model in the short term. The best model using ARIMA method is ARIMA([13]; 1; 0) with an MSE value of 1,870844. The Intervention method is a model for time series data which in practice has extreme fluctuations both up and down. In the data plot the number of train passengers was found to be extreme fluctuation. The data used was from January 2009 to June 2017 where fluctuation up significantly in January 2016 (T=85 to T=102) so the intervention model that was suspected was a step function. The best model uses the Intervention step function is ARIMA ([13]; 1; 1) (b=0; s=18; r=0) with MSE of 1124. Autoregressive Fractionally Integrated Moving Average (ARFIMA) method is a development of the ARIMA method. The advantage of the ARFIMA method is the non-integer differentiation value so that it can overcome long memory effect that can not be solve with the ARIMA method. ARFIMA model is capable of modeling high changes in the long term (long term persistence) and explain long-term and short-term correlation structures at the same time. The number of local economy class train passengers in DAOP IV Semarang contains long memory effects, so the ARFIMA method is used to obtain the best model. The best model obtained is the ARMA(0; [1,13]) model with the differential value is 0,367546, then the model can be written into ARFIMA (0; d; [1,13]) with an MSE value of 0,00964. Based on the analysis of the three methods, the best method of analyzing the number of local economy class train passengers in DAOP IV Semarang is the ARFIMA method with the model is ARFIMA (0; 0,367546; [1,13]). Keywords: Train Passengers, ARIMA, Intervention, ARFIMA, Forecasting

scholarly journals A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3561
Author(s):  
Diego Duarte ◽  
Chris Walshaw ◽  
Nadarajah Ramesh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
General Regression Neural Network ◽  
Series Data ◽  
Machine Learning Model ◽  
Hospital Patients ◽  
Major Incidents ◽  
General Regression ◽  
The Impact

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department; Number of Attendances; Unallocated Patients with a DTA (Decision to Admit); Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

Welfare Use in Japan: Trends and Determinants

2007 ◽  
pp. 88
Author(s):  
Wataru Suzuki ◽  
Yanfei Zhou
Keyword(s):  
Time Series ◽  
Longitudinal Data ◽  
Time Series Data ◽  
Positive Impact ◽  
Series Data ◽  
Negative Effects ◽  
Long Time ◽  
Welfare Use ◽  
The Impact

This article represents the first step in filling a large gap in knowledge concerning why Public Assistance (PA) use recently rose so fast in Japan. Specifically, we try to address this problem not only by performing a Blanchard and Quah decomposition on long-term monthly time series data (1960:04-2006:10), but also by estimating prefecturelevel longitudinal data. Two interesting findings emerge from the time series analysis. The first is that permanent shock imposes a continuously positive impact on the PA rate and is the main driving factor behind the recent increase in welfare use. The second finding is that the impact of temporary shock will last for a long time. The rate of the use of welfare is quite rigid because even if the PA rate rises due to temporary shocks, it takes about 8 or 9 years for it to regain its normal level. On the other hand, estimations of prefecture-level longitudinal data indicate that the Financial Capability Index (FCI) of the local government2 and minimum wage both impose negative effects on the PA rate. We also find that the rapid aging of Japan's population presents a permanent shock in practice, which makes it the most prominent contribution to surging welfare use.

scholarly journals Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 349-356
Author(s):  
J. HAZARIKA ◽  
B. PATHAK ◽  
A. N. PATOWARY
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Moving Average ◽  
Demand Management ◽  
Arima Model ◽  
Monthly Rainfall ◽  
Series Data ◽  
Data Set ◽  
Modeling And Forecasting ◽  
Moving Average Model

Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model introduced by Box and Jenkins. In this study, an attempt has been made to use Box-Jenkins methodology to build ARIMA model for monthly rainfall data taken from Dibrugarh for the period of 1980- 2014 with a total of 420 points.  We investigated and found that ARIMA (0, 0, 0) (0, 1, 1)12 model is suitable for the given data set. As such this model can be used to forecast the pattern of monthly rainfall for the upcoming years, which can help the decision makers to establish priorities in terms of agricultural, flood, water demand management etc.  

scholarly journals Minimum time required to detect population trends: the need for long-term monitoring programs

2017 ◽  
Author(s):  
Easton R White
Keyword(s):  
Time Series ◽  
Statistical Power ◽  
Time Series Data ◽  
Conservation Status ◽  
Monitoring Program ◽  
Minimum Time ◽  
Series Data ◽  
Series Length ◽  
Time Required

Long-term time series are necessary to better understand population dynamics, assess species' conservation status, and make management decisions. However, population data are often expensive, requiring a lot of time and resources. When is a population time series long enough to address a question of interest? We determine the minimum time series length required to detect significant increases or decreases in population abundance. To address this question, we use simulation methods and examine 878 populations of vertebrate species. Here we show that 15-20 years of continuous monitoring are required in order to achieve a high level of statistical power. For both simulations and the time series data, the minimum time required depends on trend strength, population variability, and temporal autocorrelation. These results point to the importance of sampling populations over long periods of time. We argue that statistical power needs to be considered in monitoring program design and evaluation. Time series less than 15-20 years are likely underpowered and potentially misleading.

scholarly journals PENGARUH ZIS (ZAKAT, INFAK, SEDEKAH) DAN ZAKAT FITRAH TERHADAP PENURUNAN KEMISKINAN DI INDONESIA PERIODE 1998 - 2010

Media Ekonomi ◽  
2017 ◽  
Vol 20 (1) ◽  
pp. 83
Author(s):  
Jumadin Lapopo
Keyword(s):  
Time Series ◽  
Developing Countries ◽  
Regression Analysis ◽  
Poverty Reduction ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Analysis Model ◽  
Independent Variables

<p>Poverty is being a problem in all developing countries including Indonesia. Among goverment programs, poverty has become the center offattention in policy at both of the regional and national levels. Looking at thephenomenon of poverty, Islam present with solution to reduce poverty through Zakat. This study aims to analyze the effect of ZIS and Zakat Fitrah against poverty in Indonesia in 1998 until 2010, data used in this study is secondary data and uses time series data, for the dependent variabel is poverty and for independent variables are ZIS and Zakat Fitrah. The analysis tools used in this study is to use multiple regression analysis model and the assumptions of classical test using the software Eviews-4. In this study also concluded that the ZIS variables significantly affect to the reduction of poverty in Indonesia although the effect is very small. In the variable Zakat Fitrah not significantly affect poverty reduction in Indonesia because of the nature of Zakat Fitrah is for consumption and not for long-term needs. The results of this study can be used for the management of zakat to be able to develop the management and to get a better system for distribution of zakat so that the main purpose of zakat can be achieved to reduce poverty.<br />Keywords : Poverty, Zakat Fitrah, ZIS.</p>

scholarly journals Track Irregularity Time Series Analysis and Trend Forecasting

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Jia Chaolong ◽  
Xu Weixiang ◽  
Wang Futian ◽  
Wang Hanning
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Short Term ◽  
Measuring Point ◽  
Trend Forecasting ◽  
Changing Trend ◽  
Track Irregularity ◽  
Linear Differential

The combination of linear and nonlinear methods is widely used in the prediction of time series data. This paper analyzes track irregularity time series data by using gray incidence degree models and methods of data transformation, trying to find the connotative relationship between the time series data. In this paper, GM(1,1)is based on first-order, single variable linear differential equations; after an adaptive improvement and error correction, it is used to predict the long-term changing trend of track irregularity at a fixed measuring point; the stochastic linear AR, Kalman filtering model, and artificial neural network model are applied to predict the short-term changing trend of track irregularity at unit section. Both long-term and short-term changes prove that the model is effective and can achieve the expected accuracy.

scholarly journals Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

2018 ◽  
Vol 7 (4.15) ◽  
pp. 25 ◽  
Author(s):  
Said Jadid Abdulkadir ◽  
Hitham Alhussian ◽  
Muhammad Nazmi ◽  
Asim A Elsheikh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.  

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