Abstract: In the automotive industry test drives are being conducted
during the development of new vehicle models or as a part of
quality assurance of series-production vehicles. The communication
on the in-vehicle network, data from external sensors, or internal
data from the electronic control units is recorded by automotive
data loggers during the test drives. The recordings are used for fault
analysis. Since the resulting data volume is tremendous, manually
analysing each recording in great detail is not feasible.
This paper proposes to use machine learning to support domainexperts
by preventing them from contemplating irrelevant data and
rather pointing them to the relevant parts in the recordings. The
underlying idea is to learn the normal behaviour from available
recordings, i.e. a training set, and then to autonomously detect
unexpected deviations and report them as anomalies.
The one-class support vector machine “support vector data description”
is utilised to calculate distances of feature vectors. SVDDSUBSEQ
is proposed as a novel approach, allowing to classify subsequences
in multivariate time series data. The approach allows to
detect unexpected faults without modelling effort as is shown with
experimental results on recordings from test drives.
Abstract: The purpose of this study was to analyze the correlation
between permitted building areas and housing distribution ratios and
their fluctuation, and test a distribution model during 3 successive governments in 5 cities including Bucheon in reference to the time
series administrative data, and thereby, interpret the results of the analysis in association with the policies pursued by the successive
governments to examine the structural fluctuation of permitted building areas and housing distribution ratios.
In order to analyze the fluctuation of permitted building areas and
housing distribution ratios during 3 successive governments and
examine the cycles of the time series data, the spectral analysis was performed, and in order to analyze the correlation between permitted
building areas and housing distribution ratios, the tabulation was performed to describe the correlations statistically, and in order to
explain about differences of fluctuation distribution of permitted building areas and housing distribution ratios among 3 governments,
the goodness of fit test was conducted.
Abstract: The RR interval series is non-stationary and unevenly
spaced in time. For estimating its power spectral density (PSD) using
traditional techniques like FFT, require resampling at uniform
intervals. The researchers have used different interpolation
techniques as resampling methods. All these resampling methods
introduce the low pass filtering effect in the power spectrum. The
lomb transform is a means of obtaining PSD estimates directly from
irregularly sampled RR interval series, thus avoiding resampling. In
this work, the superiority of Lomb transform method has been
established over FFT based approach, after applying linear and
cubicspline interpolation as resampling methods, in terms of
reproduction of exact frequency locations as well as the relative
magnitudes of each spectral component.
Abstract: In this paper we investigate the influence of external
noise on the inference of network structures. The purpose of our
simulations is to gain insights in the experimental design of microarray
experiments to infer, e.g., transcription regulatory networks
from microarray experiments. Here external noise means, that the
dynamics of the system under investigation, e.g., temporal changes of
mRNA concentration, is affected by measurement errors. Additionally
to external noise another problem occurs in the context of microarray
experiments. Practically, it is not possible to monitor the mRNA
concentration over an arbitrary long time period as demanded by the
statistical methods used to learn the underlying network structure. For
this reason, we use only short time series to make our simulations
more biologically plausible.
Abstract: All the geophysical phenomena including river
networks and flow time series are fractal events inherently and fractal
patterns can be investigated through their behaviors. A non-linear
system like a river basin can well be analyzed by a non-linear
measure such as the fractal analysis. A bilateral study is held on the
fractal properties of the river network and the river flow time series.
A moving window technique is utilized to scan the fractal properties
of them. Results depict both events follow the same strategy
regarding to the fractal properties. Both the river network and the
time series fractal dimension tend to saturate in a distinct value.
Abstract: Model Predictive Control has been previously applied
to supply chain problems with promising results; however hitherto
proposed systems possessed no information on future demand. A
forecasting methodology will surely promote the efficiency of
control actions by providing insight on the future. A complete supply
chain management framework that is based on Model Predictive
Control (MPC) and Time Series Forecasting will be presented in this
paper. The proposed framework will be tested on industrial data in
order to assess the efficiency of the method and the impact of
forecast accuracy on overall control performance of the supply chain.
To this end, forecasting methodologies with different characteristics
will be implemented on test data to generate forecasts that will serve
as input to the Model Predictive Control module.
Abstract: Using entropy weight and TOPSIS method, a
comprehensive evaluation is done on the development level of
Chinese regional service industry in this paper. Firstly, based on
existing research results, an evaluation index system is constructed
from the scale of development, the industrial structure and the
economic benefits. An evaluation model is then built up based on
entropy weight and TOPSIS, and an empirical analysis is conducted on
the development level of service industries in 31 Chinese provinces
during 2006 and 2009 from the two dimensions or time series and
cross section, which provides new idea for assessing regional service
industry. Furthermore, the 31 provinces are classified into four
categories based on the evaluation results, and deep analysis is carried
out on the evaluation results.
Abstract: Time series analysis often requires data that represents
the evolution of an observed variable in equidistant time steps. In
order to collect this data sampling is applied. While continuous
signals may be sampled, analyzed and reconstructed applying
Shannon-s sampling theorem, time-discrete signals have to be dealt
with differently. In this article we consider the discrete-event
simulation (DES) of job-shop-systems and study the effects of
different sampling rates on data quality regarding completeness and
accuracy of reconstructed inventory evolutions. At this we discuss
deterministic as well as non-deterministic behavior of system
variables. Error curves are deployed to illustrate and discuss the
sampling rate-s impact and to derive recommendations for its wellfounded
choice.
Abstract: Time series forecasting is an important and widely
popular topic in the research of system modeling. This paper
describes how to use the hybrid PSO-RLSE neuro-fuzzy learning
approach to the problem of time series forecasting. The PSO
algorithm is used to update the premise parameters of the
proposed prediction system, and the RLSE is used to update the
consequence parameters. Thanks to the hybrid learning (HL)
approach for the neuro-fuzzy system, the prediction performance
is excellent and the speed of learning convergence is much faster
than other compared approaches. In the experiments, we use the
well-known Mackey-Glass chaos time series. According to the
experimental results, the prediction performance and accuracy in
time series forecasting by the proposed approach is much better
than other compared approaches, as shown in Table IV. Excellent
prediction performance by the proposed approach has been
observed.
Abstract: This paper presents a method to estimate load profile
in a multiple power flow solutions for every minutes in 24 hours per
day. A method to calculate multiple solutions of non linear profile is
introduced. The Power System Simulation/Engineering (PSS®E) and
python has been used to solve the load power flow. The result of this
power flow solutions has been used to estimate the load profiles for
each load at buses using Independent Component Analysis (ICA)
without any knowledge of parameter and network topology of the
systems. The proposed algorithm is tested with IEEE 69 test bus
system represents for distribution part and the method of ICA has
been programmed in MATLAB R2012b version. Simulation results
and errors of estimations are discussed in this paper.
Abstract: The Bangalore City is facing the acute problem of
pollution in the atmosphere due to the heavy increase in the traffic
and developmental activities in recent years. The present study is an
attempt in the direction to assess trend of the ambient air quality
status of three stations, viz., AMCO Batteries Factory, Mysore Road,
GRAPHITE INDIA FACTORY, KHB Industrial Area, Whitefield
and Ananda Rao Circle, Gandhinagar with respect to some of the
major criteria pollutants such as Total Suspended particular matter
(SPM), Oxides of nitrogen (NOx), and Oxides of sulphur (SO2). The
sites are representative of various kinds of growths viz., commercial,
residential and industrial, prevailing in Bangalore, which are
contributing to air pollution. The concentration of Sulphur Dioxide
(SO2) at all locations showed a falling trend due to use of refined
petrol and diesel in the recent years. The concentration of Oxides of
nitrogen (NOx) showed an increasing trend but was within the
permissible limits. The concentration of the Suspended particular
matter (SPM) showed the mixed trend. The correlation between
model and observed values is found to vary from 0.4 to 0.7 for SO2,
0.45 to 0.65 for NOx and 0.4 to 0.6 for SPM. About 80% of data is
observed to fall within the error band of ±50%. Forecast test for the
best fit models showed the same trend as actual values in most of the
cases. However, the deviation observed in few cases could be
attributed to change in quality of petro products, increase in the
volume of traffic, introduction of LPG as fuel in many types of
automobiles, poor condition of roads, prevailing meteorological
conditions, etc.
Abstract: The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.
Abstract: This research focus on developing a new segmentation method for improving forecasting model which is call trend based segmentation method (TBSM). Generally, the piece-wise linear representation (PLR) can finds some of pair of trading points is well for time series data, but in the complicated stock environment it is not well for stock forecasting because of the stock has more trends of trading. If we consider the trends of trading in stock price for the trading signal which it will improve the precision of forecasting model. Therefore, a TBSM with SVR model used to detect the trading points for various stocks of Taiwanese and America under different trend tendencies. The experimental results show our trading system is more profitable and can be implemented in real time of stock market
Abstract: Embedded systems need to respect stringent real
time constraints. Various hardware components included in such
systems such as cache memories exhibit variability and therefore
affect execution time. Indeed, a cache memory access from an
embedded microprocessor might result in a cache hit where the
data is available or a cache miss and the data need to be fetched
with an additional delay from an external memory. It is therefore
highly desirable to predict future memory accesses during
execution in order to appropriately prefetch data without incurring
delays. In this paper, we evaluate the potential of several artificial
neural networks for the prediction of instruction memory
addresses. Neural network have the potential to tackle the nonlinear
behavior observed in memory accesses during program
execution and their demonstrated numerous hardware
implementation emphasize this choice over traditional forecasting
techniques for their inclusion in embedded systems. However,
embedded applications execute millions of instructions and
therefore millions of addresses to be predicted. This very
challenging problem of neural network based prediction of large
time series is approached in this paper by evaluating various neural
network architectures based on the recurrent neural network
paradigm with pre-processing based on the Self Organizing Map
(SOM) classification technique.
Abstract: Recently, a lot of attention has been devoted to
advanced techniques of system modeling. PNN(polynomial neural
network) is a GMDH-type algorithm (Group Method of Data
Handling) which is one of the useful method for modeling nonlinear
systems but PNN performance depends strongly on the number of
input variables and the order of polynomial which are determined by
trial and error. In this paper, we introduce GPNN (genetic
polynomial neural network) to improve the performance of PNN.
GPNN determines the number of input variables and the order of all
neurons with GA (genetic algorithm). We use GA to search between
all possible values for the number of input variables and the order of
polynomial. GPNN performance is obtained by two nonlinear
systems. the quadratic equation and the time series Dow Jones stock
index are two case studies for obtaining the GPNN performance.
Abstract: Data of wave height and wind speed were collected
from three existing oil fields in South China Sea – offshore
Peninsular Malaysia, Sarawak and Sabah regions. Extreme values
and other significant data were employed for analysis. The data were
recorded from 1999 until 2008. The results show that offshore
structures are susceptible to unacceptable motions initiated by wind
and waves with worst structural impacts caused by extreme wave
heights. To protect offshore structures from damage, there is a need
to quantify descriptive statistics and determine spectra envelope of
wind speed and wave height, and to ascertain the frequency content
of each spectrum for offshore structures in the South China Sea
shallow waters using measured time series. The results indicate that
the process is nonstationary; it is converted to stationary process by
first differencing the time series. For descriptive statistical analysis,
both wind speed and wave height have significant influence on the
offshore structure during the northeast monsoon with high mean wind
speed of 13.5195 knots ( = 6.3566 knots) and the high mean wave
height of 2.3597 m ( = 0.8690 m). Through observation of the
spectra, there is no clear dominant peak and the peaks fluctuate
randomly. Each wind speed spectrum and wave height spectrum has
its individual identifiable pattern. The wind speed spectrum tends to
grow gradually at the lower frequency range and increasing till it
doubles at the higher frequency range with the mean peak frequency
range of 0.4104 Hz to 0.4721 Hz, while the wave height tends to
grow drastically at the low frequency range, which then fluctuates
and decreases slightly at the high frequency range with the mean
peak frequency range of 0.2911 Hz to 0.3425 Hz.
Abstract: In this paper bi-annual time series data on unemployment rates (from the Labour Force Survey) are expanded to quarterly rates and linked to quarterly unemployment rates (from the Quarterly Labour Force Survey). The resultant linked series and the consumer price index (CPI) series are examined using Johansen’s cointegration approach and vector error correction modeling. The study finds that both the series are integrated of order one and are cointegrated. A statistically significant co-integrating relationship is found to exist between the time series of unemployment rates and the CPI. Given this significant relationship, the study models this relationship using Vector Error Correction Models (VECM), one with a restriction on the deterministic term and the other with no restriction.
A formal statistical confirmation of the existence of a unique linear and lagged relationship between inflation and unemployment for the period between September 2000 and June 2011 is presented. For the given period, the CPI was found to be an unbiased predictor of the unemployment rate. This relationship can be explored further for the development of appropriate forecasting models incorporating other study variables.
Abstract: this paper presents a multi-context recurrent network for time series analysis. While simple recurrent network (SRN) are very popular among recurrent neural networks, they still have some shortcomings in terms of learning speed and accuracy that need to be addressed. To solve these problems, we proposed a multi-context recurrent network (MCRN) with three different learning algorithms. The performance of this network is evaluated on some real-world application such as handwriting recognition and energy load forecasting. We study the performance of this network and we compared it to a very well established SRN. The experimental results showed that MCRN is very efficient and very well suited to time series analysis and its applications.
Abstract: A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This network is able to learn and optimize the rule base of a Sugeno like Fuzzy inference system using Hybrid learning algorithm, which combines gradient descent, and least mean square algorithm. This proposed neurofuzzy system has the advantage of determining the number of rules automatically and also reduce the number of rules, decrease computational time, learns faster and consumes less memory. The authors also investigate that how neurofuzzy techniques can be applied in the area of control theory to design a fuzzy controller for linear and nonlinear dynamic systems modelling from a set of input/output data. The simulation analysis on a wide range of processes, to identify nonlinear components on-linely in a control system and a benchmark problem involving the prediction of a chaotic time series is carried out. Furthermore, the well-known examples of linear and nonlinear systems are also simulated under the Matlab/Simulink environment. The above combination is also illustrated in modeling the relationship between automobile trips and demographic factors.
Abstract: Quantitative characterization of nonlinear directional
couplings between stochastic oscillators from data is considered. We
suggest coupling characteristics readily interpreted from a physical
viewpoint and their estimators. An expression for a statistical
significance level is derived analytically that allows reliable coupling
detection from a relatively short time series. Performance of the
technique is demonstrated in numerical experiments.