Abstract: Accurate forecasting of fresh produce demand is one
the challenges faced by Small Medium Enterprise (SME)
wholesalers. This paper is an attempt to understand the cause for the
high level of variability such as weather, holidays etc., in demand of
SME wholesalers. Therefore, understanding the significance of
unidentified factors may improve the forecasting accuracy. This
paper presents the current literature on the factors used to predict
demand and the existing forecasting techniques of short shelf life
products. It then investigates a variety of internal and external
possible factors, some of which is not used by other researchers in the
demand prediction process. The results presented in this paper are
further analysed using a number of techniques to minimize noise in
the data. For the analysis past sales data (January 2009 to May 2014)
from a UK based SME wholesaler is used and the results presented
are limited to product ‘Milk’ focused on café’s in derby. The
correlation analysis is done to check the dependencies of variability
factor on the actual demand. Further PCA analysis is done to
understand the significance of factors identified using correlation.
The PCA results suggest that the cloud cover, weather summary and
temperature are the most significant factors that can be used in
forecasting the demand. The correlation of the above three factors
increased relative to monthly and becomes more stable compared to
the weekly and daily demand.
Abstract: This paper addresses a cutting edge method of
business demand forecasting, based on an empirical probability
function when the historical behavior of the data is random.
Additionally, it presents error determination based on the numerical
method technique ‘propagation of errors.’ The methodology was
conducted characterization and process diagnostics demand planning
as part of the production management, then new ways to predict its
value through techniques of probability and to calculate their mistake
investigated, it was tools used numerical methods. All this based on
the behavior of the data. This analysis was determined considering
the specific business circumstances of a company in the sector of
communications, located in the city of Bogota, Colombia. In
conclusion, using this application it was possible to obtain the
adequate stock of the products required by the company to provide its
services, helping the company reduce its service time, increase the
client satisfaction rate, reduce stock which has not been in rotation
for a long time, code its inventory, and plan reorder points for the
replenishment of stock.
Abstract: The purpose of the paper is to estimate the US small
wind turbines market potential and forecast the small wind turbines
sales in the US. The forecasting method is based on the application of
the Bass model and the generalized Bass model of innovations
diffusion under replacement purchases. In the work an exponential
distribution is used for modeling of replacement purchases. Only one
parameter of such distribution is determined by average lifetime of
small wind turbines. The identification of the model parameters is
based on nonlinear regression analysis on the basis of the annual
sales statistics which has been published by the American Wind
Energy Association (AWEA) since 2001 up to 2012. The estimation
of the US average market potential of small wind turbines (for
adoption purchases) without account of price changes is 57080
(confidence interval from 49294 to 64866 at P = 0.95) under average
lifetime of wind turbines 15 years, and 62402 (confidence interval
from 54154 to 70648 at P = 0.95) under average lifetime of wind
turbines 20 years. In the first case the explained variance is 90,7%,
while in the second - 91,8%. The effect of the wind turbines price
changes on their sales was estimated using generalized Bass model.
This required a price forecast. To do this, the polynomial regression
function, which is based on the Berkeley Lab statistics, was used. The
estimation of the US average market potential of small wind turbines
(for adoption purchases) in that case is 42542 (confidence interval
from 32863 to 52221 at P = 0.95) under average lifetime of wind
turbines 15 years, and 47426 (confidence interval from 36092 to
58760 at P = 0.95) under average lifetime of wind turbines 20 years.
In the first case the explained variance is 95,3%, while in the second
– 95,3%.
Abstract: In this paper the problem of the application of
temporal reasoning and case-based reasoning in intelligent decision
support systems is considered. The method of case-based reasoning
with temporal dependences for the solution of problems of real-time
diagnostics and forecasting in intelligent decision support systems is
described. This paper demonstrates how the temporal case-based
reasoning system can be used in intelligent decision support systems
of the car access control. This work was supported by RFBR.
Abstract: The research of juice flavor forecasting has become
more important in China. Due to the fast economic growth in China,
many different kinds of juices have been introduced to the market. If a
beverage company can understand their customers’ preference well,
the juice can be served more attractive. Thus, this study intends to
introducing the basic theory and computing process of grapes juice
flavor forecasting based on support vector regression (SVR). Applying
SVR, BPN, and LR to forecast the flavor of grapes juice in real data
shows that SVR is more suitable and effective at predicting
performance.
Abstract: Taiwan is a hyper endemic area for the Hepatitis B
virus (HBV). The estimated total number of HBsAg carriers in the
general population who are more than 20 years old is more than 3
million. Therefore, a case record review is conducted from January
2003 to June 2007 for all patients with a diagnosis of acute hepatitis
who were admitted to the Emergency Department (ED) of a
well-known teaching hospital. The cost for the use of medical
resources is defined as the total medical fee. In this study, principal
component analysis (PCA) is firstly employed to reduce the number of
dimensions. Support vector regression (SVR) and artificial neural
network (ANN) are then used to develop the forecasting model. A total
of 117 patients meet the inclusion criteria. 61% patients involved in
this study are hepatitis B related. The computational result shows that
the proposed PCA-SVR model has superior performance than other
compared algorithms. In conclusion, the Child-Pugh score and
echogram can both be used to predict the cost of medical resources for
patients with acute hepatitis in the ED.
Abstract: A model was constructed to predict the amount of
solar radiation that will make contact with the surface of the earth in
a given location an hour into the future. This project was supported
by the Southern Company to determine at what specific times during
a given day of the year solar panels could be relied upon to produce
energy in sufficient quantities. Due to their ability as universal
function approximators, an artificial neural network was used to
estimate the nonlinear pattern of solar radiation, which utilized
measurements of weather conditions collected at the Griffin, Georgia
weather station as inputs. A number of network configurations and
training strategies were utilized, though a multilayer perceptron with
a variety of hidden nodes trained with the resilient propagation
algorithm consistently yielded the most accurate predictions. In
addition, a modeled direct normal irradiance field and adjacent
weather station data were used to bolster prediction accuracy. In later
trials, the solar radiation field was preprocessed with a discrete
wavelet transform with the aim of removing noise from the
measurements. The current model provides predictions of solar
radiation with a mean square error of 0.0042, though ongoing efforts
are being made to further improve the model’s accuracy.
Abstract: Traditional document representation for classification
follows Bag of Words (BoW) approach to represent the term weights.
The conventional method uses the Vector Space Model (VSM) to
exploit the statistical information of terms in the documents and they
fail to address the semantic information as well as order of the terms
present in the documents. Although, the phrase based approach
follows the order of the terms present in the documents rather than
semantics behind the word. Therefore, a semantic concept based
approach is used in this paper for enhancing the semantics by
incorporating the ontology information. In this paper a novel method
is proposed to forecast the intraday stock market price directional
movement based on the sentiments from Twitter and money control
news articles. The stock market forecasting is a very difficult and
highly complicated task because it is affected by many factors such
as economic conditions, political events and investor’s sentiment etc.
The stock market series are generally dynamic, nonparametric, noisy
and chaotic by nature. The sentiment analysis along with wisdom of
crowds can automatically compute the collective intelligence of
future performance in many areas like stock market, box office sales
and election outcomes. The proposed method utilizes collective
sentiments for stock market to predict the stock price directional
movements. The collective sentiments in the above social media have
powerful prediction on the stock price directional movements as
up/down by using Granger Causality test.
Abstract: Modeling and forecasting dynamics of rainfall
occurrences constitute one of the major topics, which have been
largely treated by statisticians, hydrologists, climatologists and many
other groups of scientists. In the same issue, we propose, in the
present paper, a new hybrid method, which combines Extreme
Values and fractal theories. We illustrate the use of our methodology
for transformed Emberger Index series, constructed basing on data
recorded in Oujda (Morocco).
The index is treated at first by Peaks Over Threshold (POT)
approach, to identify excess observations over an optimal threshold u.
In the second step, we consider the resulting excess as a fractal object
included in one dimensional space of time. We identify fractal
dimension by the box counting. We discuss the prospect descriptions
of rainfall data sets under Generalized Pareto Distribution, assured by
Extreme Values Theory (EVT). We show that, despite of the
appropriateness of return periods given by POT approach, the
introduction of fractal dimension provides accurate interpretation
results, which can ameliorate apprehension of rainfall occurrences.
Abstract: The effects of hypertension are often lethal thus its
early detection and prevention is very important for everybody. In
this paper, a neural network (NN) model was developed and trained
based on a dataset of hypertension causative parameters in order to
forecast the likelihood of occurrence of hypertension in patients. Our
research goal was to analyze the potential of the presented NN to
predict, for a period of time, the risk of hypertension or the risk of
developing this disease for patients that are or not currently
hypertensive. The results of the analysis for a given patient can
support doctors in taking pro-active measures for averting the
occurrence of hypertension such as recommendations regarding the
patient behavior in order to lower his hypertension risk. Moreover,
the paper envisages a set of three example scenarios in order to
determine the age when the patient becomes hypertensive, i.e.
determine the threshold for hypertensive age, to analyze what
happens if the threshold hypertensive age is set to a certain age and
the weight of the patient if being varied, and, to set the ideal weight
for the patient and analyze what happens with the threshold of
hypertensive age.
Abstract: The aim of this paper is to select the most accurate
forecasting method for predicting the future values of the
unemployment rate in selected European countries. In order to do so,
several forecasting techniques adequate for forecasting time series
with trend component, were selected, namely: double exponential
smoothing (also known as Holt`s method) and Holt-Winters` method
which accounts for trend and seasonality. The results of the empirical
analysis showed that the optimal model for forecasting
unemployment rate in Greece was Holt-Winters` additive method. In
the case of Spain, according to MAPE, the optimal model was double
exponential smoothing model. Furthermore, for Croatia and Italy the
best forecasting model for unemployment rate was Holt-Winters`
multiplicative model, whereas in the case of Portugal the best model
to forecast unemployment rate was Double exponential smoothing
model. Our findings are in line with European Commission
unemployment rate estimates.
Abstract: This research was conducted in the Mae Sot
Watershed where located in the Moei River Basin at the Upper
Salween River Basin in Tak Province, Thailand. The Mae Sot
Municipality is the largest urban area in Tak Province and situated in
the midstream of the Mae Sot Watershed. It usually faces flash flood
problem after heavy rain due to poor flood management has been
reported since economic rapidly bloom up in recent years. Its
catchment can be classified as ungauged basin with lack of rainfall
data and no any stream gaging station was reported. It was attached
by most severely flood events in 2013 as the worst studied case for
all those communities in this municipality. Moreover, other problems
are also faced in this watershed, such shortage water supply for
domestic consumption and agriculture utilizations including a
deterioration of water quality and landslide as well. The research
aimed to increase capability building and strengthening the
participation of those local community leaders and related agencies to
conduct better water management in urban area was started by mean
of the data collection and illustration of the appropriated application
of some short period rainfall forecasting model as they aim for better
flood relief plan and management through the hydrologic model
system and river analysis system programs. The authors intended to
apply the global rainfall data via the integrated data viewer (IDV)
program from the Unidata with the aim for rainfall forecasting in a
short period of 7-10 days in advance during rainy season instead of
real time record. The IDV product can be present in an advance
period of rainfall with time step of 3-6 hours was introduced to the
communities. The result can be used as input data to the hydrologic
modeling system model (HEC-HMS) for synthesizing flood
hydrographs and use for flood forecasting as well. The authors
applied the river analysis system model (HEC-RAS) to present flood
flow behaviors in the reach of the Mae Sot stream via the downtown
of the Mae Sot City as flood extents as the water surface level at
every cross-sectional profiles of the stream. Both models of HMS and
RAS were tested in 2013 with observed rainfall and inflow-outflow
data from the Mae Sot Dam. The result of HMS showed fit to the
observed data at the dam and applied at upstream boundary discharge
to RAS in order to simulate flood extents and tested in the field, and
the result found satisfying. The product of rainfall from IDV was fair
while compared with observed data. However, it is an appropriate
tool to use in the ungauged catchment to use with flood hydrograph
and river analysis models for future efficient flood relief plan and
management.
Abstract: Load modeling is one of the central functions in
power systems operations. Electricity cannot be stored, which means
that for electric utility, the estimate of the future demand is necessary
in managing the production and purchasing in an economically
reasonable way. A majority of the recently reported approaches are
based on neural network. The attraction of the methods lies in the
assumption that neural networks are able to learn properties of the
load. However, the development of the methods is not finished, and
the lack of comparative results on different model variations is a
problem. This paper presents a new approach in order to predict the
Tunisia daily peak load. The proposed method employs a
computational intelligence scheme based on the Fuzzy neural
network (FNN) and support vector regression (SVR). Experimental
results obtained indicate that our proposed FNN-SVR technique gives
significantly good prediction accuracy compared to some classical
techniques.
Abstract: The purpose of this study is to forecast the influences
of information and communication technology (ICT) on the structural
changes of Japanese economies. In this study, input-output (IO) and
statistical approaches are used as analysis instruments. More
specifically, this study employs Leontief IO coefficients and
constrained multivariate regression (CMR) model in order to achieve
the purpose. The periods of initial and forecast in this study are 2005
and 2015, respectively. In this study, ICT is represented by ICT capital
stocks. This study conducts two levels of analysis, namely macro and
micro. The results of macro level analysis show that the dynamics of
Japanese economies on the forecast period, relative to the initial period,
are not so high. We focus on (1) commerce, (2) business services and
office supplies, and (3) personal services sectors when conducting the
analysis of the micro level. Further, we analyze its specific IO
coefficients when doing this analysis. The results of the analysis
explain that ICT gives a strong influence on the changes of these
coefficients from initial to forecast periods.
Abstract: The rate of natural gas dissociation from the Coal
Matrix depends on depressurization of reservoir through removing of
the cleat water from the coal seam. These waters are similar to brine
and aged of very long years. For improving the connectivity through
fracking /fracturing, high pressure liquids are pumped off inside the
coal body. A significant quantity of accumulated water, a combined
mixture of cleat water and fracking fluids (back flow water) is
pumped out through gas well. In Queensland, Australia Coal Seam
Gas (CSG) industry is in booming state and estimated of 30,000 wells
would be active for CSG production forecasting life span of 30 years.
Integrated water management along with water softening programs is
practiced for subsequent treatment and later on discharge to nearby
surface water catchment. Water treatment is an important part of the
CSG industry. A case study on a CSG site and review on the test
results are discussed for assessing the Standards & Practices for
management of CSG by-product water and their subsequent disposal
activities. This study was directed toward (i) water management and
softening process in Spring Gully CSG field, (ii) Comparative
analysis on experimental study and standards and (iii) Disposal of the
treated water. This study also aimed for alternative usages and their
impact on vegetation, living species as well as long term effects.
Abstract: At present, the evaluation of voltage stability
assessment experiences sizeable anxiety in the safe operation of
power systems. This is due to the complications of a strain power
system. With the snowballing of power demand by the consumers
and also the restricted amount of power sources, therefore, the system
has to perform at its maximum proficiency. Consequently, the
noteworthy to discover the maximum ability boundary prior to
voltage collapse should be undertaken. A preliminary warning can be
perceived to evade the interruption of power system’s capacity. The
effectiveness of line voltage stability indices (LVSI) is differentiated
in this paper. The main purpose of the indices used is to predict the
proximity of voltage instability of the electric power system. On the
other hand, the indices are also able to decide the weakest load buses
which are close to voltage collapse in the power system. The line
stability indices are assessed using the IEEE 14 bus test system to
validate its practicability. Results demonstrated that the implemented
indices are practically relevant in predicting the manifestation of
voltage collapse in the system. Therefore, essential actions can be
taken to dodge the incident from arising.
Abstract: In this paper, we study the rainfall using a time series
for weather stations in Nakhon Ratchasima province in Thailand by
various statistical methods to enable us to analyse the behaviour of
rainfall in the study areas. Time-series analysis is an important tool in
modelling and forecasting rainfall. The ARIMA and Holt-Winter
models were built on the basis of exponential smoothing. All the
models proved to be adequate. Therefore it is possible to give
information that can help decision makers establish strategies for the
proper planning of agriculture, drainage systems and other water
resource applications in Nakhon Ratchasima province. We obtained
the best performance from forecasting with the ARIMA
Model(1,0,1)(1,0,1)12.
Abstract: Fuzzy systems have been successfully used for
exchange rate forecasting. However, fuzzy system is very confusing
and complex to be designed by an expert, as there is a large set of
parameters (fuzzy knowledge base) that must be selected, it is not a
simple task to select the appropriate fuzzy knowledge base for an
exchange rate forecasting. The researchers often look the effect of
fuzzy knowledge base on the performances of fuzzy system
forecasting. This paper proposes a genetic fuzzy predictor to forecast
the future value of daily US Dollar/Euro exchange rate time’s series.
A range of methodologies based on a set of fuzzy predictor’s which
allow the forecasting of the same time series, but with a different
fuzzy partition. Each fuzzy predictor is built from two stages, where
each stage is performed by a real genetic algorithm.
Abstract: Load Forecasting plays a key role in making today's
and future's Smart Energy Grids sustainable and reliable. Accurate
power consumption prediction allows utilities to organize in advance
their resources or to execute Demand Response strategies more
effectively, which enables several features such as higher
sustainability, better quality of service, and affordable electricity
tariffs. It is easy yet effective to apply Load Forecasting at larger
geographic scale, i.e. Smart Micro Grids, wherein the lower available
grid flexibility makes accurate prediction more critical in Demand
Response applications. This paper analyses the application of
short-term load forecasting in a concrete scenario, proposed within the
EU-funded GreenCom project, which collect load data from single
loads and households belonging to a Smart Micro Grid. Three
short-term load forecasting techniques, i.e. linear regression, artificial
neural networks, and radial basis function network, are considered,
compared, and evaluated through absolute forecast errors and training
time. The influence of weather conditions in Load Forecasting is also
evaluated. A new definition of Gain is introduced in this paper, which
innovatively serves as an indicator of short-term prediction
capabilities of time spam consistency. Two models, 24- and
1-hour-ahead forecasting, are built to comprehensively compare these
three techniques.
Abstract: Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. Interval Type 2 Fuzzy Logic System (IT2 FLS), with additional degrees of freedom, gives an excellent tool for handling uncertainties and it improved the prediction accuracy. The training data used in this study covers the period from January 1, 2012 to February 1, 2012 for winter season and the period from July 1, 2012 to August 1, 2012 for summer season. The actual load forecasting period starts from January 22, till 28, 2012 for winter model and from July 22 till 28, 2012 for summer model. The real data for Iraqi power system which belongs to the Ministry of Electricity.