Progress Risk Analysis Based on the Improved Learning Curve

2012 ◽  
Vol 174-177 ◽  
pp. 3313-3317
Author(s):  
Wen Ping Wu ◽  
Jian Dong Feng ◽  
Yang Zhang
Keyword(s):  
Time Series ◽  
Risk Analysis ◽  
Learning Curve ◽  
The State ◽  
Forecasting Model ◽  
Policy Makers ◽  
Scientific Analysis ◽  
Improved Learning ◽  
Random Variance

The learning curve is the curve which indicates individuals and organizations’ effectiveness of learning. Reasonable learning curve not only can use scientific analysis the time-consuming of process changes, accurately forecast the end of time, but also can help policy makers identify risks and risk analysis of the incident on the progress of the project impact.This article summarized the results of previous studies, constructed forecasting model of dynamic random variance time series, based on the learning curve, explained the state of volatility of learning curve in the construction of projects.

Country Risk Analysis of Bangladesh

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Zahidur Rahman ◽  
Jannatul Ferdous Bristy
Keyword(s):  
Risk Analysis ◽  
Financial Risk ◽  
Political Risk ◽  
Multinational Companies ◽  
Country Risk ◽  
Low Risk ◽  
Primary Data ◽  
Policy Makers ◽  
Financial Factors ◽  
Political Economic

In the endeavor of conquering the worlds consumers, multinational companies face enormous risks. Such risks may arise from different political, economic, and financial factors. These factors are commonly referred to country risk as a whole. Focusing Bangladesh in this regard, objective of this study is to find out the level of country risk in terms of political, economic, and financial riskiness. Analysis of country risk has been done using an internationally recognized methodology named International Country Risk Guide (ICRG). For political risk analysis, primary data has been collected from 20 journalists, bureaucrats and policy makers, business persons, corporate professionals, and academicians with a structured closed-ended questionnaire. Results indicate that Bangladesh is in high risk position in terms of political risk, low risk position in terms of economic risk and very low risk position in terms of financial risk. Compositely, Bangladesh has been found to be a moderately risky country for investment.

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

scholarly journals Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽  
2021 ◽  
Vol 19 (1) ◽  
pp. 360-374
Author(s):  
Yuan Pei ◽  
Lei Zhenglin ◽  
Zeng Qinghui ◽  
Wu Yixiao ◽  
Lu Yanli ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Load Forecasting ◽  
Series Data ◽  
Forecasting Model ◽  
Combined Model ◽  
Forecasting Accuracy ◽  
Forecasting Method ◽  
Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

scholarly journals Hybrid forecasting model for non-linear time series

2020 ◽  
Author(s):  
E. Priyadarshini ◽  
G. Raj Gayathri ◽  
M. Vidhya ◽  
A. Govindarajan ◽  
Samuel Chakkravarthi
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Forecasting Model ◽  
Non Linear ◽  
Hybrid Forecasting

scholarly journals Vitamin D, SARS-CoV-2 and Causal Associations in Transversal Studies: The Time-Series Analysis to Reveal Potential Confounders. Comment on Gaudio et al. Vitamin D Levels Are Reduced at the Time of Hospital Admission in Sicilian SARS-CoV-2-Positive Patients. Int. J. Environ. Res. Public Health 2021, 18, 3491

2021 ◽  
Vol 18 (13) ◽  
pp. 6793
Author(s):  
Cristiano Ialongo ◽  
Antonella Farina ◽  
Raffaella Labriola ◽  
Antonio Angeloni ◽  
Emanuela Anastasi
Keyword(s):  
Public Health ◽  
Vitamin D ◽  
Time Series ◽  
Severe Acute Respiratory Syndrome ◽  
Time Series Analysis ◽  
Hospital Admission ◽  
The State ◽  
Series Analysis ◽  
Vitamin D Levels ◽  
Time Of Admission

We read with great interest the paper by Gaudio and colleagues on vitamin D and on the state of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the time of admission [...]

scholarly journals Phase Synchronization Is the Amplified Result by the Hilbert Transform

2015 ◽  
Vol 2015 ◽  
pp. 1-3 ◽  
Author(s):  
Ming-Chi Lu ◽  
Hsing-Chung Ho ◽  
Chen-An Chan ◽  
Chia-Ju Liu ◽  
Jiann-Shing Lih ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Hilbert Transform ◽  
Phase Synchronization ◽  
Nonlinear Dynamical Systems ◽  
The State ◽  
Nonlinear Dynamical ◽  
Large Correlation ◽  
Current Usage ◽  
The Hilbert Transform

We investigate the interplay between phase synchronization and amplitude synchronization in nonlinear dynamical systems. It is numerically found that phase synchronization intends to be established earlier than amplitude synchronization. Nevertheless, amplitude synchronization (or the state with large correlation between the amplitudes) is crucial for the maintenance of a high correlation between two time series. A breakdown of high correlation in amplitudes will lead to a desynchronization of two time series. It is shown that these unique features are caused essentially by the Hilbert transform. This leads to a deep concern and criticism on the current usage of phase synchronization.

scholarly journals Chaotic Patterns in Aeroelastic Signals

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
F. D. Marques ◽  
R. M. G. Vasconcellos
Keyword(s):  
Time Series ◽  
State Space ◽  
Complex Dynamics ◽  
Chaotic Behavior ◽  
Separated Flow ◽  
Direct Analysis ◽  
Surrogate Data ◽  
The State ◽  
Aeroelastic System ◽  
Value Decomposition

This work presents the analysis of nonlinear aeroelastic time series from wing vibrations due to airflow separation during wind tunnel experiments. Surrogate data method is used to justify the application of nonlinear time series analysis to the aeroelastic system, after rejecting the chance for nonstationarity. The singular value decomposition (SVD) approach is used to reconstruct the state space, reducing noise from the aeroelastic time series. Direct analysis of reconstructed trajectories in the state space and the determination of Poincaré sections have been employed to investigate complex dynamics and chaotic patterns. With the reconstructed state spaces, qualitative analyses may be done, and the attractors evolutions with parametric variation are presented. Overall results reveal complex system dynamics associated with highly separated flow effects together with nonlinear coupling between aeroelastic modes. Bifurcations to the nonlinear aeroelastic system are observed for two investigations, that is, considering oscillations-induced aeroelastic evolutions with varying freestream speed, and aeroelastic evolutions at constant freestream speed and varying oscillations. Finally, Lyapunov exponent calculation is proceeded in order to infer on chaotic behavior. Poincaré mappings also suggest bifurcations and chaos, reinforced by the attainment of maximum positive Lyapunov exponents.

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