This document presents research on developing early warning signals to predict periods of excessive price volatility for rice. The researcher uses daily rice future prices from 1991 to 2013 to identify 9 clusters when price volatility was extreme. An objective function is created to optimize early warning signal parameters like window of observation, quantile level, frequency of breach, and scope. Particle swarm optimization is used to minimize the function and obtain optimal parameters. The resulting early warning signals have 94.4% accuracy, cover 99.4% of extreme volatility periods, and provide an average 43 day lead time before volatility clusters, with 61 days for severe clusters.
If you have ever been a caregiver in Arizona, you know how much the people you serve rely on you. When Staff are trained and know what to expect as well as what is expected of them, everybody wins. This Training Material is for Group Home Managers as well as Assisted living Staff and mangers. Let's listen more and remember, Volcanic Enthusiasm !!!
If you have ever been a caregiver in Arizona, you know how much the people you serve rely on you. When Staff are trained and know what to expect as well as what is expected of them, everybody wins. This Training Material is for Group Home Managers as well as Assisted living Staff and mangers. Let's listen more and remember, Volcanic Enthusiasm !!!
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and common problems for production planning problem to optimize. In this study, one of the mathematical models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is efficient method to solve continues non linear problem. Moreover, mentioned models of production planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
Application of Regression and Neural Network Models in Computing Forecasts fo...iosrjce
In this study, a quadratic regression model and a two layered layer recurrent neural network
(TLLRNN) method were used to model forecasting performance of the daily crude oil production data of the
Nigerian National Petroleum Corporation (NNPC). The two methods were applied on the difference series and
log difference series of the NNPC series. The results indicates that the two layered layer recurrent neural
network model have better forecasting per-formance greater than the quadratic regression method based on the
mean error square sense. The root mean square error (RMSE) and the mean absolute error (MAE) were applied
to ascertain the assertion that the two layered layer recurrent neural network method have better forecasting
performance greater than the quadratic regression method. The outcome of the analysis also indicates that
modeling forecasting performance of the NNPC data with the log dif-ference series of the data gives greater
forecasting performances than modeling with the difference series of the NNPC data irrespective of the method
used in modeling with the series. These results were achieved from 1 day ahead pre-dictions, 3 days ahead
predictions and 5 days ahead predictions for 50 days sample length, 100 days sample length, 200 days sample
length, 400 days sample length and 800 days sample length. Autocorrelation functions emerging from the
increment series, that is, difference series and log difference series of the daily crude oil production data of the
NNPC indicates significant autocorrelations and significant partial autocorrelations. The data used in this
study is a time series data obtained from the daily crude oil production of the Nigerian National Petroleum
Corporation (NNPC) for a period of six years (1st January, 2008 - 31st December, 2013). The analysis for this
study was simulated using MATLAB software, version 8.03
Design of Kalman filter for Airborne ApplicationsIJERA Editor
Today multiple multi-sensor airborne surveillance systems are available which comprises of primary radar and
secondary surveillance radar as the active sensor on board. The electronics and communication support measure
system (ECSMS) will aid in identification, detection and classification of targets. These systems will detect,
identify, classify the different threats present in the surveillance area and supports defense operation. These
systems contain multiple functional operations as detection of air borne and surface target, tracking, and Multisensor
data fusion. This paper presents the multi-sensor data fusion technique and how to detect and track
moving target in the surveillance area.
Modeling adoptions and the stages of the diffusion of innovationsNicola Barbieri
We study the data mining problem of modeling adoptions and the stages of the diffusion of an innovation. For our aim we propose a stochastic model which decomposes a diffusion trace (sequence of adoptions) in an ordered sequence of stages, where each stage is intuitively built around two dimensions: users and relative speed at which adoptions happen. Each stage is characterized by a specific rate of adoption and it involves different users to different extent, while the sequentiality in the diffusion is guaranteed by constraining the transition probabilities among stages.
An empirical evaluation on synthetic and real-world adoption logs shows the effectiveness of the proposed framework in summarizing the adoption process, enabling several analysis tasks such as the identification of adopter categories, clustering and characterization of diffusion traces, and prediction of which users will adopt an item in the next future.
Wearable Accelerometer Optimal Positions for Human Motion Recognition(LifeTec...sugiuralab
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The History of Cooking Oil Fortification in Indonesia: Government Support for the Program and Challenges by Idrus Jus’at, Senior Lecturer, Esa Unggul University, Indonesia. Presented at the ReSAKSS-Asia - MIID conference "Evolving Agrifood Systems in Asia: Achieving food and nutrition security by 2030" on Oct 30-31, 2019 in Yangon, Myanmar.
Food Fortification Policies in the Asia Region by Dennis Bittisnich, Food Fortification Initiative. Presented at the ReSAKSS-Asia - MIID conference "Evolving Agrifood Systems in Asia: Achieving food and nutrition security by 2030" on Oct 30-31, 2019 in Yangon, Myanmar.
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THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and common problems for production planning problem to optimize. In this study, one of the mathematical models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is efficient method to solve continues non linear problem. Moreover, mentioned models of production planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo Algorithm is suitable choice for optimization in convergence of solution.
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
THE STUDY OF CUCKOO OPTIMIZATION ALGORITHM FOR PRODUCTION PLANNING PROBLEMijcax
Constrained Nonlinear programming problems are hard problems, and one of the most widely used and
common problems for production planning problem to optimize. In this study, one of the mathematical
models of production planning is survey and the problem solved by cuckoo algorithm. Cuckoo Algorithm is
efficient method to solve continues non linear problem. Moreover, mentioned models of production
planning solved with Genetic algorithm and Lingo software and the results will compared. The Cuckoo
Algorithm is suitable choice for optimization in convergence of solution
Application of Regression and Neural Network Models in Computing Forecasts fo...iosrjce
In this study, a quadratic regression model and a two layered layer recurrent neural network
(TLLRNN) method were used to model forecasting performance of the daily crude oil production data of the
Nigerian National Petroleum Corporation (NNPC). The two methods were applied on the difference series and
log difference series of the NNPC series. The results indicates that the two layered layer recurrent neural
network model have better forecasting per-formance greater than the quadratic regression method based on the
mean error square sense. The root mean square error (RMSE) and the mean absolute error (MAE) were applied
to ascertain the assertion that the two layered layer recurrent neural network method have better forecasting
performance greater than the quadratic regression method. The outcome of the analysis also indicates that
modeling forecasting performance of the NNPC data with the log dif-ference series of the data gives greater
forecasting performances than modeling with the difference series of the NNPC data irrespective of the method
used in modeling with the series. These results were achieved from 1 day ahead pre-dictions, 3 days ahead
predictions and 5 days ahead predictions for 50 days sample length, 100 days sample length, 200 days sample
length, 400 days sample length and 800 days sample length. Autocorrelation functions emerging from the
increment series, that is, difference series and log difference series of the daily crude oil production data of the
NNPC indicates significant autocorrelations and significant partial autocorrelations. The data used in this
study is a time series data obtained from the daily crude oil production of the Nigerian National Petroleum
Corporation (NNPC) for a period of six years (1st January, 2008 - 31st December, 2013). The analysis for this
study was simulated using MATLAB software, version 8.03
Design of Kalman filter for Airborne ApplicationsIJERA Editor
Today multiple multi-sensor airborne surveillance systems are available which comprises of primary radar and
secondary surveillance radar as the active sensor on board. The electronics and communication support measure
system (ECSMS) will aid in identification, detection and classification of targets. These systems will detect,
identify, classify the different threats present in the surveillance area and supports defense operation. These
systems contain multiple functional operations as detection of air borne and surface target, tracking, and Multisensor
data fusion. This paper presents the multi-sensor data fusion technique and how to detect and track
moving target in the surveillance area.
Modeling adoptions and the stages of the diffusion of innovationsNicola Barbieri
We study the data mining problem of modeling adoptions and the stages of the diffusion of an innovation. For our aim we propose a stochastic model which decomposes a diffusion trace (sequence of adoptions) in an ordered sequence of stages, where each stage is intuitively built around two dimensions: users and relative speed at which adoptions happen. Each stage is characterized by a specific rate of adoption and it involves different users to different extent, while the sequentiality in the diffusion is guaranteed by constraining the transition probabilities among stages.
An empirical evaluation on synthetic and real-world adoption logs shows the effectiveness of the proposed framework in summarizing the adoption process, enabling several analysis tasks such as the identification of adopter categories, clustering and characterization of diffusion traces, and prediction of which users will adopt an item in the next future.
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Food Fortification Policies in the Asia Region by Dennis Bittisnich, Food Fortification Initiative. Presented at the ReSAKSS-Asia - MIID conference "Evolving Agrifood Systems in Asia: Achieving food and nutrition security by 2030" on Oct 30-31, 2019 in Yangon, Myanmar.
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Predicting Time Periiods of Excessive Price Volatility: The Case of Rice- Ramon Clarete and Alfonso Labao
1. Predicting Time Periods of Excessive Price Volatility:
The Case of Rice
Dr. Ramon Clarete Alfonso Labao
University of the Philippines, School of Economics
September 25, 2013
5. Overview:
Importance of being forewarned of extreme food price-volatility:
provides time to undertake cooperation
prevent herding and self-fulfilling crises
avoid repetition of 2008 rice crisis
prevent welfare costs for the poor
6. Overview:
Importance of being forewarned of extreme food price-volatility:
provides time to undertake cooperation
prevent herding and self-fulfilling crises
avoid repetition of 2008 rice crisis
prevent welfare costs for the poor
G20 report stresses importance of accurate and timely market information
22. Methodology:
MTY’s Two-step method:
Construct a nonparametric trend via spline-backfitted-kernel:
rt = m0 +
d
a=1
ma(Xta) + (h0 +
d
a=1
ha(Xta)1/2
) t
Estimate the high-order 95 percent quantile via Generalized Pareto Distribution (GPD):
ˆqt(α) = ˆk+1 +
ˆβt
ˆγt
((
(1 − α)
(k/N)
)−ˆγt
− 1)
23. Methodology:
MTY’s Two-step method:
Construct a nonparametric trend via spline-backfitted-kernel:
rt = m0 +
d
a=1
ma(Xta) + (h0 +
d
a=1
ha(Xta)1/2
) t
Estimate the high-order 95 percent quantile via Generalized Pareto Distribution (GPD):
ˆqt(α) = ˆk+1 +
ˆβt
ˆγt
((
(1 − α)
(k/N)
)−ˆγt
− 1)
Estimated 95 percent Conditional Quantiles:
ˆqt(α/rt−1, rt−2) = ˜m(rt−1, rt−2) + [(˜h(rt−1, rt−2))1/2
ˆqt(α)]
24. How are Time Periods of Excessive Price Volatility (EPV) defined?
25. How are Time Periods of Excessive Price Volatility (EPV) defined?
Martins-Filho, Maximo Torero and Feng Yao’s definition of time periods of EPV:
EPV Definition:
Time-Periods whereby the preceding 60 days experienced a significantly high amount
of extreme positive price returns over the 95 percent conditional quantile.
26. How are Time Periods of Excessive Price Volatility (EPV) defined?
Martins-Filho, Maximo Torero and Feng Yao’s definition of time periods of EPV:
EPV Definition:
Time-Periods whereby the preceding 60 days experienced a significantly high amount
of extreme positive price returns over the 95 percent conditional quantile.
... at least 7 instances of extreme positive price returns within a span of 60 days
27. 1st (Jul 1993 - Feb 1994) and 2nd (Jul 1994 - Oct 1994) EPV cluster:
28. 1st (Jul 1993 - Feb 1994) and 2nd (Jul 1994 - Oct 1994) EPV cluster:
3rd (Mar 1999 - Sep 1999) and 4th (Jun 2001 - Aug 2001) EPV cluster:
29. 5th (Jul 2002 - Aug 2002) and 6th (May 2003 - Oct 2003) EPV cluster:
30. 5th (Jul 2002 - Aug 2002) and 6th (May 2003 - Oct 2003) EPV cluster:
7th (Mar 2008 - Nov 2008) and 8th (Apr 2009 - May 2009) EPV cluster:
47. Next Task: Looking for a Good Early Warning Signal
Parameters of the Early Warning Signals:
48. Next Task: Looking for a Good Early Warning Signal
Parameters of the Early Warning Signals:
Window of Observation
49. Next Task: Looking for a Good Early Warning Signal
Parameters of the Early Warning Signals:
Window of Observation
Lower-Order Quantile Level
50. Next Task: Looking for a Good Early Warning Signal
Parameters of the Early Warning Signals:
Window of Observation
Lower-Order Quantile Level
Frequency of Breach
51. Next Task: Looking for a Good Early Warning Signal
Parameters of the Early Warning Signals:
Window of Observation
Lower-Order Quantile Level
Frequency of Breach
Scope
52. Next Task: Looking for a Good Early Warning Signal
Parameters of the Early Warning Signals:
Window of Observation
Lower-Order Quantile Level
Frequency of Breach
Scope
Together, these parameters generate a time-based variable, namely...
53. Next Task: Looking for a Good Early Warning Signal
... the scope’s time-coverage
55. What makes a good early warning signal:
accurate: scope’s time-coverage covers an EPV cluster, minimal false alarms
56. What makes a good early warning signal:
accurate: scope’s time-coverage covers an EPV cluster, minimal false alarms
good lead time: there’s reasonable lead time before EPV cluster
57. What makes a good early warning signal:
accurate: scope’s time-coverage covers an EPV cluster, minimal false alarms
good lead time: there’s reasonable lead time before EPV cluster
comprehensive: signals pre-empt almost all EPV clusters
58. What makes a good early warning signal:
accurate: scope’s time-coverage covers an EPV cluster, minimal false alarms
good lead time: there’s reasonable lead time before EPV cluster
comprehensive: signals pre-empt almost all EPV clusters
Basically...
We create a new set of trends (using spbk) and solve for parameters that meet above
requirements
59. We expressed the criteria (accurate, comprehensive, good lead time), into a single
additive objective function that can be minimized:
Objective Function for Minimization:
f (X) = [(
count1
laghvp
) ∗ 1.35] + [
count2
hvp
] + [(accuracy) ∗ 1.35]+
[(
totalews
totaldays
) ∗ 1.25] + [
totalscope
totaldays
]
60. We expressed the criteria (accurate, comprehensive, good lead time), into a single
additive objective function that can be minimized:
Objective Function for Minimization:
f (X) = [(
count1
laghvp
) ∗ 1.35] + [
count2
hvp
] + [(accuracy) ∗ 1.35]+
[(
totalews
totaldays
) ∗ 1.25] + [
totalscope
totaldays
]
Where:
count1: total no. of lagged mirror-images of time periods of EPV not covered by
the scopes of the signals
laghvp: total no. of lagged mirror-images of time periods of EPV
count2: total no. of actual time periods of EPV not covered by the scopes’
time-coverage of the early warning signals
hvp: total no. of actual time periods of EPV
accuracy: predictive accuracy of the early warning signal with following ratio:
[1-((no.of high.vol.periods captured by scope’s time-coverage) / (hvp)].
total ews: total no. of early warning signals
total scope: total no. of days covered by the scopes’ time-coverage of the early
warning signals
total days: total no. of days within the sample timeframe (1991 to 2013): 5407
future days
61. We expressed the criteria (accurate, comprehensive, good lead time), into a single
additive objective function that can be minimized:
Objective Function for Minimization:
f (X) = [(
count1
laghvp
) ∗ 1.35] + [
count2
hvp
] + [(accuracy) ∗ 1.35]+
[(
totalews
totaldays
) ∗ 1.25] + [
totalscope
totaldays
]
62. We expressed the criteria (accurate, comprehensive, good lead time), into a single
additive objective function that can be minimized:
Objective Function for Minimization:
f (X) = [(
count1
laghvp
) ∗ 1.35] + [
count2
hvp
] + [(accuracy) ∗ 1.35]+
[(
totalews
totaldays
) ∗ 1.25] + [
totalscope
totaldays
]
First two (2) terms - optimize good lead-time and comprehensiveness
63. We expressed the criteria (accurate, comprehensive, good lead time), into a single
additive objective function that can be minimized:
Objective Function for Minimization:
f (X) = [(
count1
laghvp
) ∗ 1.35] + [
count2
hvp
] + [(accuracy) ∗ 1.35]+
[(
totalews
totaldays
) ∗ 1.25] + [
totalscope
totaldays
]
First two (2) terms - optimize good lead-time and comprehensiveness
Last three (3) terms - minimize false alarms and improve accuracy.
64. We use R’s PSO package to minimize the objective function...
65. We use R’s PSO package to minimize the objective function...
PSO: Particle Swarm Optimization
PSO is a meta-heuristic designed for functions that are hard to optimize using
traditional optimization procedures (due to several peaks, non-linearities, long-forms,
etc..)
66. We use R’s PSO package to minimize the objective function...
PSO: Particle Swarm Optimization
PSO is a meta-heuristic designed for functions that are hard to optimize using
traditional optimization procedures (due to several peaks, non-linearities, long-forms,
etc..)
PSO Parameter Search Range:
67. We use R’s PSO package to minimize the objective function...
PSO: Particle Swarm Optimization
PSO is a meta-heuristic designed for functions that are hard to optimize using
traditional optimization procedures (due to several peaks, non-linearities, long-forms,
etc..)
PSO Parameter Search Range:
Window of Observation: from 1 to 40 future days
Quantile Level: from 50 percent to 99 percent quantile
Frequency of Breach: from 1 to 5
Scope of Early Warning Signal: from 60 to 250 future days
69. After 1000 iterations, we obtain the following optimal solution:
Optimal Solution:
Window of Observation: 11.61 future days
Quantile Level: 91.31 percent
Frequency of Breach: 4.18
Scope of Early Warning Signal: 107.06 future days
70. After 1000 iterations, we obtain the following optimal solution:
Optimal Solution:
Window of Observation: 11.61 future days
Quantile Level: 91.31 percent
Frequency of Breach: 4.18
Scope of Early Warning Signal: 107.06 future days
Given these parameters, an early warning signal will be activated...
71. After 1000 iterations, we obtain the following optimal solution:
Optimal Solution:
Window of Observation: 11.61 future days
Quantile Level: 91.31 percent
Frequency of Breach: 4.18
Scope of Early Warning Signal: 107.06 future days
Given these parameters, an early warning signal will be activated...
... once the 91.31 percent conditional quantile is breached five (5) times within
11.61 future days..
73. Just a review...
What makes a good early warning signal:
accurate: scope’s time-coverage covers an EPV cluster, minimal false alarms
good lead time: there’s reasonable lead time before EPV cluster
comprehensive: signals pre-empt almost all EPV clusters
75. We obtain these Performance Statistics:
accuracy of ews: 94.44 percent predictive power
comprehensiveness: 99.40 percent of time periods of EPV are covered
lead-time before EPV cluster: average of 43 days from the earliest signal to start of
high-volatility time-period
76. Below is a summary of the different EPV clusters and the lead-times of the ews:
77. Below is a summary of the different EPV clusters and the lead-times of the ews:
Total of 125 early warning signals activated from Sep 1991 to Mar 2013.
78. Below is a summary of the different EPV clusters and the lead-times of the ews:
Total of 125 early warning signals activated from Sep 1991 to Mar 2013.
This is only 2.3 percent of the time.
79. Below is a summary of the different EPV clusters and the lead-times of the ews:
Average of 43 days between earliest ews and start of an EPV cluster
80. Below is a summary of the different EPV clusters and the lead-times of the ews:
Average of 43 days between earliest ews and start of an EPV cluster
But among the four (4) severe EPV clusters, there is an average lead-time of 61
days
81. Below is a summary of the different EPV clusters and the lead-times of the ews:
Average of 43 days between earliest ews and start of an EPV cluster
But among the four (4) severe EPV clusters, there is an average lead-time of 61
days
The unflagged 2011 EPV cluster is not severe..
82. Below is a summary of the different EPV clusters and the lead-times of the ews:
Average of 43 days between earliest ews and start of an EPV cluster
But among the four (4) severe EPV clusters, there is an average lead-time of 61
days
The unflagged 2011 EPV cluster is not severe..
All EPV are sufficiently covered from beginning till end by the scopes of the
ews..