Use of Temporal Irreversibility of Symbolic Time Series for Early Detection of Extinction in Thermal Pulse Combustors

2006 ◽  
Author(s):  
Subhashis Datta ◽  
Achintya Mukhopadhyay ◽  
Dipankar Sanyal
Keyword(s):  
Time Series ◽  
Fourth Order ◽  
Time Series Data ◽  
Order System ◽  
Series Data ◽  
Thermal Pulse ◽  
Pulse Combustor ◽  
The Difference ◽  
Symbol Sequences ◽  
System Approaches

A nonlinear fourth-order dynamic model of a thermal pulse combustor has been developed. In this work, the time series data generated by solution of the fourth order system is converted into a set of symbols based on the values of pressure variables. The key step to symbolization involves transformation of the original values to a stream of discretised symbols by partitioning the range of observed values into a finite number of regions and then assigning a symbol to each measurement based on the region in which it falls. Once all the measured values are symbolized, a symbol sequence vector consisting of L successive temporal observations is defined and its relative frequency is determined. In this work, the relative frequencies of different symbol sequences are computed by scanning the time series data in forward and reverse directions. The difference between the relative frequencies obtained in forward and reverse scanning is termed as "irreversibility" of the process. It is observed that for given alphabet and word sizes, the "irreversibility" increases as the system approaches extinction. The effects of different choices of alphabet and word sizes are also considered.

Effects of Tailpipe Friction on the Nonlinear Dynamics of a Thermal Pulse Combustor

2008 ◽  
Vol 130 (1) ◽  
Author(s):  
Achintya Mukhopadhyay ◽  
Subhashis Datta ◽  
Dipankar Sanyal
Keyword(s):  
Time Series ◽  
Friction Factor ◽  
Time Series Data ◽  
Threshold Value ◽  
Series Data ◽  
Combustion Dynamics ◽  
Thermal Pulse ◽  
Pulse Combustor ◽  
Steady Combustion ◽  
Peak Pressures

The effect of tailpipe friction on the combustion dynamics inside a thermal pulse combustor has been investigated using a nonlinear model consisting of four coupled first order ordinary differential equations. The dynamics of the system is represented through time series plots, time-delay phase plots, and Poincaré maps. The results indicate that as the tailpipe friction factor is lowered, the system undergoes a transition from steady combustion through oscillating combustion to an intermittent combustion with chaotic characteristics before extinction. The time series data are shown to be useful indicator for early detection of extinction. In one approach (thresholding), the occurrence of local peak pressures below a predefined threshold value is identified as an event and the number of events (event count) and largest number of successive cycles with such events (event duration) are recorded as the friction factor is lowered. In another approach, the statistical moments (kurtosis) of the data are used. Number of kurtosis peaks above a prescribed value and variance of the kurtosis values are recorded for decreasing values of friction factor. All these numbers sharply increase as the system approaches extinction.

Self-Tuning Varri Method in Preparing Fatigue Segment

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
M. H. Osman ◽  
Z. M. Nopiah ◽  
S. Abdullah ◽  
A. Lennie
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Training Dataset ◽  
Series Data ◽  
Additional Advantage ◽  
Local Maxima ◽  
Conventional Procedure ◽  
Computational Operation ◽  
Self Tuning ◽  
The Difference

An overlapping segmentation method on time series data is often used for preparing training dataset i.e. the population of instance, for classification data mining. Having large number of redundant instances would burden the training process with heavy computational operation. This would happen if practitioners fail to acknowledge an appropriate amount of overlap when performing the time series segmentation. Fortunately, the risk could be decreased if knowledge preferences can be determined to guide on overlapping criteria in the segmentation algorithm. Thus, this study aims to investigate how the Varri method is able to contribute for better understanding in preparing training dataset consists of irredundant fatigue segment from the loading history (fatigue signal). Generally, the method locates segment boundaries based on local maxima in the difference function which are above the assigned threshold. In the present study, the mean and standard deviation have been used to define the function due to the fact that predicting attributes are the key components in defining instance redundancy. The resulting dataset from the proposed method is trained by three classification algorithms under the supervision of the Genetic algorithms-based feature selection wrapper approach. The average performance index shows an additional advantage of the proposed method as compared to the conventional procedure in preparing training dataset.

Asymmetries in financial returns

2017 ◽  
Vol 04 (04) ◽  
pp. 1750045 ◽  
Author(s):  
Dilip B. Madan ◽  
King Wang
Keyword(s):  
Time Series ◽  
Rate Of Convergence ◽  
Time Series Data ◽  
Gamma Process ◽  
Series Data ◽  
Convergence To Equilibrium ◽  
Financial Returns ◽  
Delta Hedging ◽  
The Difference

Market clichés assert that markets take escalators up and elevators down. The observation suggests differentiating models for up and down moves. Non-diffusive models allow for this and we model the move as the difference of two independent mean reverting increasing processes driven by gamma process shocks. The model is estimated on time series data as well as option data. Broadly speaking, the rise occurs with more frequent and smaller jumps with a faster rate of convergence to equilibrium. The down tick process has larger, less frequent moves with longer memories. Applications to delta hedging and the setting of profit targets and stop losses are also presented.

Structural condition assessment using entropy-based time series analysis

2017 ◽  
Vol 28 (14) ◽  
pp. 1941-1956 ◽  
Author(s):  
Mehrisadat Makki Alamdari ◽  
Bijan Samali ◽  
Jianchun Li ◽  
Ye Lu ◽  
Samir Mustapha
Keyword(s):  
Time Series ◽  
State Space ◽  
Structural Damage ◽  
Time Series Data ◽  
National Laboratory ◽  
The State ◽  
Frame Structure ◽  
Series Data ◽  
Symbol Space ◽  
Symbol Sequences

We present a time-series-based algorithm to identify structural damage in the structure. The method is in the context of non-model-based approaches; hence, it eliminates the need of any representative numerical model of the structure to be built. The method starts by partitioning the state space into a finite number of subsets which are mutually exclusive and exhaustive and each subset is identified by a distinct symbol. Partitioning is performed based on a maximum entropy approach which takes into account the sparsity and distribution of information in the time series. After constructing the symbol space, the time series data are uniquely transformed from the state space into the constructed symbol space to create the symbol sequences. Symbol sequences are the simplified abstractions of the complex system and describe the evolution of the system. Each symbol sequence is statistically characterized by its entropy which is obtained based on the probability of occurrence of the symbols in the sequence. As a consequence of damage occurrence, the entropy of the symbol sequences changes; this change is implemented to define a damage indicative feature. The method shows promising results using data from two experimental case studies subject to varying excitation. The first specimen is a reinforced concrete jack arch which replicates one of the major structural components of the Sydney Harbor Bridge and the second specimen is a three-story frame structure model which has been tested at Los Alamos National Laboratory. The method not only could successfully identify the presence of damage but also has potential to localize it.

scholarly journals Contracting Institutions, Property Rights and Economic Growth in the Post-90s Albania

2018 ◽  
Vol 14 (1) ◽  
pp. 176 ◽  
Author(s):  
Mario Curcija
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Economic Development ◽  
Property Rights ◽  
Time Series Data ◽  
Series Data ◽  
Significant Positive Effect ◽  
The Difference ◽  
Positive Effect

Economists often emphasize the role of institutions in order to explain the difference in wealth and development among different countries and in their researches they mark correlation between institution and economic development. This paper tests the validity of these models referring to Albania using time-series data from 1993 to 2015. There is evidence of significant positive effect of property rights on economic growth and credit to private sector, while there is evidenced insignificant impact of contracting institutions on economic outputs. A plausible explanation of these differences may be the different flexibility towards changes on property right institution rather than contracting institutions.

scholarly journals Prediksi Barang Keluar TB. Wijaya Bangunan Menggunakan Algoritma KNN Regression dengan RStudio

2021 ◽  
Vol 6 (2) ◽  
pp. 90-97
Author(s):  
Natcha Kwintarini Suparman ◽  
Budi Arif Dermawan ◽  
Tesa Nur Padilah
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Predictive Accuracy ◽  
Series Data ◽  
Mean Square ◽  
Data Preparation ◽  
Distance Calculation ◽  
Collection Data ◽  
The Difference ◽  
Existing Data

TB. Wijaya Bangunan is a business entity that has weaknesses in managing inventories. This study aims to help TB. Wijaya Bangunan in managing inventory based on existing data reduce the difference between the number of incoming goods and the number of outgoing goods. The methods used are data collection, data preparation, data selection, preprocessing, data transformation, distance calculation, calculation of predictions, evaluation, and display of prediction results using a Shiny framework. This study uses the Time Series KNN Regression algorithm to predict the number of outgoing goods based on time series data with existing data. The most predicted results came out in the 9th week period as much as 22.40%. Based on the process that has been done, it can be concluded that the evaluation value of Root Mean Square Error (RMSE) is at least 3.55, which means it has the best predictive accuracy results.

scholarly journals PENGARUH SUKU BUNGA, PRODUK DOMESTIK BRUTO DAN NILAI TUKAR TERHADAP EKSPOR DI INDONESIA

2019 ◽  
Vol 4 (2) ◽  
pp. 300-317
Author(s):  
Okta Rabiana Risma ◽  
T. Zulham ◽  
Taufiq C. Dawood
Keyword(s):  
Time Series ◽  
Interest Rate ◽  
Gross Domestic Product ◽  
Interest Rates ◽  
Time Series Data ◽  
Domestic Product ◽  
Series Data ◽  
Auto Regressive ◽  
The Difference

This research aims to analyze the level of exports in Indonesia by using Time Series data from the year 1990 to 2015 against a variable interest rate loands, gross domestic product, and the exchange rate. Methods of analysis used i.e, Auto Regressive Distributed Lagged (ARDL). The results showed that the three variables have no Granger which is caused by the difference of the order on the test stasioner. Based on a test of wald for the short term that gained and the long-term gross domestic product, exchange rates and interest rates significantly influential credit toward export.Keywords:ARDL, export, interest rate loands, gross domestic product, exchange rates.AbstrakPenelitian ini bertujuan untuk menganalisis tingkat ekspor di Indonesia dengan menggunakan data Time Series dari tahun 1990 sampai 2015 terhadap variabel suku bunga kredit, produk domestik bruto, dan nilai tukar. Metode analisis yang digunakan yaitu AutoRegressive Distributed Lagged (ARDL).Hasil penelitian menunjukkan bahwa ketiga variabel tidak memiliki kointegrasi yang disebabkan oleh perbedaan ordo pada uji stasionernya. Berdasarkan uji wald didapat bahwa untuk jangka pendek dan jangka panjang produk domestik bruto, nilai tukar dan suku bunga kredit berpengaruh secara signifikan terhadap ekspor.

Optimization of Time-Series Data Partitioning for Parameter Identification

2010 ◽  
Author(s):  
Soumik Sarkar ◽  
Kushal Mukherjee ◽  
Xin Jin ◽  
Asok Ray
Keyword(s):  
Time Series ◽  
Parameter Identification ◽  
Time Series Data ◽  
Nonlinear Dynamical System ◽  
Data Partitioning ◽  
Set Partitioning ◽  
Series Data ◽  
Data Set ◽  
Partitioning Procedure ◽  
Symbol Sequences

This paper presents a data-driven method of parameter identification in nonlinear systems based on the theories of symbolic dynamics. Although construction of finite-state-machine models from symbol sequences has been widely reported, similar efforts have not been expended to investigate partitioning of time series data to optimally generate symbol sequences. A data-set partitioning procedure is proposed to extract features from time series data by optimizing a multi-objective cost functional. Performance of the optimal partitioning procedure is compared with those of other traditional partitioning (e.g., uniform and maximum entropy) schemes. Then, tools of pattern classification are applied to identify the ranges of multiple parameters of a well-known chaotic nonlinear dynamical system, namely the Duffing Equation, from its time series response.

An intuitive skewness-based symmetry test applicable to stationary time series data

2019 ◽  
Vol 23 (5) ◽  
Author(s):  
Luke Hartigan
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Stationary Time Series ◽  
Series Data ◽  
Finite Sample ◽  
Test Statistic ◽  
Stationary Time ◽  
Symmetry Test ◽  
Finite Sample Properties ◽  
The Difference

Abstract I propose a simple skewness-based test of symmetry suitable for a stationary time series. The test is based on the difference between the squared deviation of a process above its median with that below it. The test has many attractive features: it is applicable to weakly dependent processes, it has a familiar form, it can be implemented using regression, and it has a standard Gaussian limiting distribution under the null hypothesis of symmetry. The finite sample properties of the test statistic are examined via Monte Carlo simulation and suggest that it has better size-adjusted power compared to competing tests in the literature when examining moderately persistence processes. I apply the test to a range of US economic and financial data and find stronger support for asymmetry in financial series compared to economic series.

scholarly journals Outlier Detection in Climatology Time Series with Sliding Window Prediction

2019 ◽  
Vol 8 (10) ◽  
pp. 966-970
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Prediction Method ◽  
Sliding Window ◽  
Threshold Value ◽  
Series Data ◽  
Quality Of Data ◽  
The Difference ◽  
Complete Operation

It is important to identify outliers for climatology series data. With better quality of data decision capability will improve which in turn will improve the complete operation. An algorithm utilising the sliding window prediction method is being proposed to improve the data decision capability in this paper. The time series are parted in accordance with the size of sliding window. Thereafter a prediction model is rooted with the help of historical data to forecast the new values. There is a pre decided threshold value which will be compared to the difference of predicted and measured value. If the difference is greater than a predefined threshold then the specific point will be treated as an outlier. Results from experiment are showing that the algorithm is identifying the outliers in climatology time series data and also remodeling the correction efficiency.

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