scholarly journals A single-station prediction model as a contribution to instantaneous mapping

1994 ◽  
Vol 37 (2) ◽  
Author(s):  
I. Stanislawska
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Method ◽  
Physical Processes ◽  
The Past ◽  
Ionospheric Station ◽  
Single Station ◽  
Daily Forecast

The paper presents two opposite approaches for single-station prediction and forecast. Both methods are based on different assumptions of physical processes in the ionosphere and need the different set of incoming data. Different heliogeophysical data, mainly f0F2 parameters from the past were analyzed for f0F2 obtaining for the requested period ahead. In the first method - the autocovariance prediction method - the time series of f0F2 from one station are used for daily forecast at that point. The second method may be used for obtaining f0F2 not only at the particular ionospheric station, but also at any point within the considered area.

scholarly journals Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Hua Li ◽  
Dongmei Mu ◽  
Ping Wang ◽  
Yin Li ◽  
Dongxuan Wang
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Seasonal Changes ◽  
Prediction Models ◽  
Patient Flow ◽  
Prediction Method ◽  
Hospital Management ◽  
Medical Resources ◽  
Obstetric Patient ◽  
The Government

Objective: Given the ever-changing flow of obstetric patients in the hospital, how the government and hospital management plan and allocate medical resources has become an important problem that needs to be urgently solved. In this study a prediction method for calculating the monthly and daily flow of patients based on time series is proposed to provide decision support for government and hospital management.Methods: The historical patient flow data from the Department of Obstetrics and Gynecology of the First Hospital of Jilin University, China, from January 1, 2018, to February 29, 2020, were used as the training set. Seven models such as XGBoost, SVM, RF, and NNAR were used to predict the daily patient flow in the next 14 days. The HoltWinters model is then used to predict the monthly flow of patients over the next year.Results: The results of this analysis and prediction model showed that the obstetric inpatient flow was not a purely random process, and that patient flow was not only accompanied by the random patient flow but also showed a trend change and seasonal change rule. ACF,PACF,Ljung_box, and residual histogram were then used to verify the accuracy of the prediction model, and the results show that the Holtwiners model was optimal. R2, MAPE, and other indicators were used to measure the accuracy of the 14 day prediction model, and the results showed that HoltWinters and STL prediction models achieved high accuracy.Conclusion: In this paper, the time series model was used to analyze the trend and seasonal changes of obstetric patient flow and predict the patient flow in the next 14 days and 12 months. On this basis, combined with the trend and seasonal changes of obstetric patient flow, a more reasonable and fair horizontal allocation scheme of medical resources is proposed, combined with the prediction of patient flow.

Prediction of Financial Time-Series Signals Using á Trous Wavelet Transform

2015 ◽  
Vol 781 ◽  
pp. 523-526 ◽  
Author(s):  
Wassanun Sangjun ◽  
Supawat Supakwong ◽  
Suttipong Thajchayapong
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Polynomial Regression ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Prediction Method ◽  
Real World Data ◽  
Financial Time ◽  
The Past ◽  
Significant Performance

This paper proposes a financial time-series prediction method consisting of á Trous wavelet transform and polynomial regression. The main purpose of employing á Trous wavelet transform is to decompose financial time-series signals into different resolutions where only relevant signal components are used for prediction. Also, á Trous wavelet transform is used to avoid the edge problem where only the past and present components of the time-series signal are taken into account. The decomposed time-series signals are then fed into the polynomial regression part to obtain predicted time-series signals. Using real-world data, performance evaluation is conducted based on total benefit and profit/loss where it is shown that á Trous wavelet transform contributes to a significant performance improvement.

An Application of the Analysis and Forecasting Model Based on Time Series – Safety Monitoring of the Reservoir Dams at XiaShan

2012 ◽  
Vol 201-202 ◽  
pp. 582-585
Author(s):  
Rui Lian Hou
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Safety Monitoring ◽  
Forecasting Model ◽  
Average Value ◽  
The Past ◽  
Model Method ◽  
Curve Model ◽  
Technical Theory ◽  
Accuracy Of Prediction

Adopting the sliding average value method, curve model method in the analytic approach of the array of time, we have established a time series model of the analysis and forecasting model and given an application at Xiashan Reservoir dams which can monitor the safety of the dams. In this paper we firstly have introduced the relevant technical theory of the time series, including the methods, concepts, principles,and so on. Then we have created a time series forecasting model, and finally we apply the model to Xiashan Reservoir Dams. We have predicted the data of the coming year at the same time through historical monitoring data of the past three years.And we have verified the accuracy of prediction model and adjusted the related parameters.

scholarly journals Multivariate Nonlinear Analysis and Prediction of Shanghai Stock Market

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
Junhai Ma ◽  
Lixia Liu
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Stock Returns ◽  
Stock Price ◽  
Linear Prediction ◽  
Stock Exchange ◽  
Multivariate Time Series ◽  
Prediction Method ◽  
Nonlinear Prediction ◽  
Stock Price Prediction

This study attempts to characterize and predict stock returns series in Shanghai stock exchange using the concepts of nonlinear dynamical theory. Surrogate data method of multivariate time series shows that all the stock returns time series exhibit nonlinearity. Multivariate nonlinear prediction methods and univariate nonlinear prediction method, all of which use the concept of phase space reconstruction, are considered. The results indicate that multivariate nonlinear prediction model outperforms univariate nonlinear prediction model, local linear prediction method of multivariate time series outperforms local polynomial prediction method, and BP neural network method. Multivariate nonlinear prediction model is a useful tool for stock price prediction in emerging markets.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

scholarly journals Evaluating the performance of multivariate indicators of resilience loss

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Els Weinans ◽  
Rick Quax ◽  
Egbert H. van Nes ◽  
Ingrid A. van de Leemput
Keyword(s):  
Time Series ◽  
Complex Systems ◽  
Multivariate Time Series ◽  
Tipping Points ◽  
Physical And Mental Health ◽  
One Dimensional ◽  
Slowing Down ◽  
The Past ◽  
Technological Developments ◽  
Resilience Indicators

AbstractVarious complex systems, such as the climate, ecosystems, and physical and mental health can show large shifts in response to small changes in their environment. These ‘tipping points’ are notoriously hard to predict based on trends. However, in the past 20 years several indicators pointing to a loss of resilience have been developed. These indicators use fluctuations in time series to detect critical slowing down preceding a tipping point. Most of the existing indicators are based on models of one-dimensional systems. However, complex systems generally consist of multiple interacting entities. Moreover, because of technological developments and wearables, multivariate time series are becoming increasingly available in different fields of science. In order to apply the framework of resilience indicators to multivariate time series, various extensions have been proposed. Not all multivariate indicators have been tested for the same types of systems and therefore a systematic comparison between the methods is lacking. Here, we evaluate the performance of the different multivariate indicators of resilience loss in different scenarios. We show that there is not one method outperforming the others. Instead, which method is best to use depends on the type of scenario the system is subject to. We propose a set of guidelines to help future users choose which multivariate indicator of resilience is best to use for their particular system.

scholarly journals Developing an Enhanced Short-Range Railroad Track Condition Prediction Model for Optimal Maintenance Scheduling

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Peng Xu ◽  
Chuanjun Jia ◽  
Ye Li ◽  
Quanxin Sun ◽  
Rengkui Liu
Keyword(s):  
Prediction Model ◽  
Short Range ◽  
Prediction Method ◽  
Maintenance Scheduling ◽  
Railroad Track ◽  
Track Geometry ◽  
Track Maintenance ◽  
Track Condition ◽  
Optimal Maintenance ◽  
Process Work

As railroad infrastructure becomes older and older and rail transportation is developing towards higher speed and heavier axle, the risk to safe rail transport and the expenses for railroad maintenance are increasing. The railroad infrastructure deterioration (prediction) model is vital to reducing the risk and the expenses. A short-range track condition prediction method was developed in our previous research on railroad track deterioration analysis. It is intended to provide track maintenance managers with two or three months of track condition in advance to schedule track maintenance activities more smartly. Recent comparison analyses on track geometrical exceptions calculated from track condition measured with track geometry cars and those predicted by the method showed that the method fails to provide reliable condition for some analysis sections. This paper presented the enhancement to the method. One year of track geometry data for the Jiulong-Beijing railroad from track geometry cars was used to conduct error analyses and comparison analyses. Analysis results imply that the enhanced model is robust to make reliable predictions. Our in-process work on applying those predicted conditions for optimal track maintenance scheduling is discussed in brief as well.

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