AN EVOLUTIONARY APPROACH FOR IMPUTING MISSING DATA IN TIME SERIES

2010 ◽  
Vol 19 (01) ◽  
pp. 107-121 ◽  
Author(s):  
JUAN CARLOS FIGUEROA GARCÍA ◽  
DUSKO KALENATIC ◽  
CESAR AMILCAR LÓPEZ BELLO
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Missing Data ◽  
Genetic Structure ◽  
Evolutionary Algorithm ◽  
Fitness Function ◽  
Error Function ◽  
Missing Observations ◽  
Mean And Variance ◽  
Methodological Aspects

This paper presents a proposal based on an evolutionary algorithm for imputing missing observations in time series. A genetic algorithm based on the minimization of an error function derived from their autocorrelation function, mean, and variance is presented. All methodological aspects of the genetic structure are presented. An extended description of the design of the fitness function is provided. Four application examples are provided and solved by using the proposed method.

PALMPRINTS: A COOPERATIVE CO-EVOLUTIONARY ALGORITHM FOR CLUSTERING HAND IMAGES

2005 ◽  
Vol 05 (03) ◽  
pp. 595-616 ◽  
Author(s):  
NAWWAF KHARMA ◽  
CHING Y. SUEN ◽  
PEI F. GUO
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithm ◽  
Practical Problem ◽  
Research Effort ◽  
Fitness Function ◽  
Image Clustering ◽  
Number Of Clusters ◽  
Fitness Value ◽  
Evolutionary Genetic ◽  
Hand Image

The main objective of Project PalmPrints is to develop and demonstrate a special co-evolutionary genetic algorithm (GA) that optimizes (a clustering fitness function) with respect to three quantities, (a) the dimensions of the clustering space; (b) the number of clusters; and (c) and the locations of the various clusters. This genetic algorithm is applied to the specific practical problem of hand image clustering, with success. In addition to the above, this research effort makes the following contributions: (i) a CD database of (raw and processed) right-hand images; (ii) a number of novel features designed specifically for hand image classification; (iii) an extended fitness function, which is particularly suited to a dynamic (i.e. dimensionality varying) clustering space. Despite the complexity of the multi-optimizational task, the results of this study are clear. The GA succeeded in achieving a maximum fitness value of 99.1%; while reducing the number of dimensions (features) of the space by more than half (from 84 to 41).

scholarly journals Time series analysis on precipitation with missing data using stochastic SARIMA

MAUSAM ◽  
2021 ◽  
Vol 71 (4) ◽  
pp. 617-624
Author(s):  
SHARMA M. K. ◽  
OMER MOHAMMED ◽  
KIANI SARA
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Analysis ◽  
Stochastic Model ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Missing Observations ◽  
Full Data ◽  
Sarima Model ◽  
Monthly Precipitation Data

This paper presents an application of the Box-Jenkins methodology for modeling the precipitation in Iran. Linear stochastic model known as multiplicative seasonal ARIMA was used to model the monthly precipitation data for 44 years. Missing data occurred in between for 34 months for some reason. To fill the gap a SARIMA model was fitted based on the first 180 available observations and the missing observations were substituted by the forecasts for the next 34 months. Then a SARIMA model was fitted for the full data. The result showed that the fitted model represent the full data well.

Detection of failures of technical systems by forecasting time series

2020 ◽  
pp. 40-44
Author(s):  
Timofey G. Kaplunov ◽  
Viktor M. Kureychik ◽  
Elizaveta N. Abramova
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Fitness Function ◽  
Technical System ◽  
Time Intervals ◽  
General Direction ◽  
Predictive Algorithm ◽  
Predictive Algorithms ◽  
System States ◽  
Alternative Solutions

The paper considers the algorithm of diagnostics of the technical system States at future time points by extrapolation of the results of current observations using a genetic algorithm. The novelty of this work lies in the fact that the proposed algorithm is able to generate predictions of technical system States using predictive algorithms and mathematical rules. The paper offers an original view on the use of genetic algorithm as an independent predictive algorithm. The described algorithm is a combination of a modified genetic algorithm and a number of mathematical rules. So, as a modification of the genetic algorithm, its parallel variant (island model) is used, and as a fitness function, a function is used that tests new alternative solutions for distance from the geometric representation of the averaged values of the time series graph. The algorithm performs prediction for a given number of time intervals ahead, for processes that are affected by a limited number of external factors. The efficiency of the algorithm was confirmed by an experiment, which resulted in a predictive solution, the General direction of the process (which was tested in practice).

Stock market data modelling using fitness-oriented JAYA algorithm-based deep belief network

Kybernetes ◽  
2019 ◽  
Vol 49 (9) ◽  
pp. 2309-2334
Author(s):  
A. Kullaya Swamy ◽  
Sarojamma B.
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Fitness Function ◽  
Error Function ◽  
Percentage Error ◽  
Jaya Algorithm ◽  
Market Data ◽  
Content Type ◽  
Proposed Model ◽  
L2 Norm

Purpose Data mining plays a major role in forecasting the open price details of the stock market. However, it fails to address the dimensionality and expectancy of a naive investor. Hence, this paper aims to study a future prediction model named time series model is implemented. Design/methodology/approach In this model, the stock market data are fed to the proposed deep neural networks (DBN), and the number of hidden neurons is optimized by the modified JAYA Algorithm (JA), based on the fitness function. Hence, the algorithm is termed as fitness-oriented JA (FJA), and the proposed model is termed as FJA-DBN. The primary objective of this open price forecasting model is the minimization of the error function between the modeled and actual output. Findings The performance analysis demonstrates that the deviation of FJA–DBN in predicting the open price details of the Tata Motors, Reliance Power and Infosys data shows better performance in terms of mean error percentage, symmetric mean absolute percentage error, mean absolute scaled error, mean absolute error, root mean square error, L1-norm, L2-Norm and Infinity-Norm (least infinity error). Research limitations/implications The proposed model can be used to forecast the open price details. Practical implications The investors are constantly reviewing past pricing history and using it to influence their future investment decisions. There are some basic assumptions used in this analysis, first being that everything significant about a company is already priced into the stock, other being that the price moves in trends Originality/value This paper presents a technique for time series modeling using JA. This is the first work that uses FJA-based optimization for stock market open price prediction.

scholarly journals Prediction of Chaotic Time Series Based on BEN-AGA Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
LiYun Su ◽  
Fan Yang
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Network Model ◽  
Fitness Function ◽  
Activation Function ◽  
Single Step ◽  
Chaotic Time Series ◽  
Adaptive Genetic Algorithm ◽  
Observation Sequence ◽  
The Brain

Aiming at the prediction problem of chaotic time series, this paper proposes a brain emotional network combined with an adaptive genetic algorithm (BEN-AGA) model to predict chaotic time series. First, we improve the emotional brain learning (BEL) model using the activation function to change the two linear structures the amygdala and the orbitofrontal cortex into the nonlinear structure, and then we establish the brain emotional network (BEN) model. The brain emotional network model has stronger nonlinear calculation ability and generalization ability. Next, we use the adaptive genetic algorithm to optimize the parameters of the brain emotional network model. The weights to be optimized in the model are coded as chromosomes. We design the dynamic crossover probability and mutation probability to control the crossover process and the mutation process, and the optimal parameters are selected through the fitness function to evaluate the chromosome. In this way, we increase the approximation capability of the model and increase the calculation speed of the model. Finally, we reconstruct the phase space of the observation sequence based on the short-term predictability of the chaotic time series; then we establish a brain emotional network model and optimize its parameters with an adaptive genetic algorithm and perform a single-step prediction on the optimized model to obtain the prediction error. The model proposed in this paper is applied to the prediction of Rossler chaotic time series and sunspot chaotic time series. The experimental results verify the effectiveness of the BEN-AGA model and show that this model has higher prediction accuracy and more stability than other methods.

HETEROSKEDASTICITY AUTOCORRELATION ROBUST INFERENCE IN TIME SERIES REGRESSIONS WITH MISSING DATA

2018 ◽  
Vol 35 (03) ◽  
pp. 601-629
Author(s):  
Seung-Hwa Rho ◽  
Timothy J. Vogelsang
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Regression Model ◽  
Critical Values ◽  
Missing Observations ◽  
Test Statistics ◽  
Time Series Regression ◽  
Bootstrap Methods ◽  
Time Periods ◽  
Equal Space

In this article, we investigate the properties of heteroskedasticity and autocorrelation robust (HAR) test statistics in time series regression settings when observations are missing. We primarily focus on the nonrandom missing process case where we treat the missing locations to be fixed asT→ ∞ by mapping the missing and observed cutoff dates into points on [0,1] based on the proportion of time periods in the sample that occur up to those cutoff dates. We consider two models, the amplitude modulated series (Parzen, 1963) regression model, which amounts to plugging in zeros for missing observations, and the equal space regression model, which simply ignores the missing observations. When the amplitude modulated series regression model is used, the fixed-blimits of the HAR test statistics depend on the locations of missing observations but are otherwise pivotal. When the equal space regression model is used, the fixed-blimits of the HAR test statistics have the standard fixed-blimits as in Kiefer and Vogelsang (2005). We discuss methods for obtaining fixed-bcritical values with a focus on bootstrap methods and find the naivei.i.d.bootstrap with missing dates fixed to be an effective and practical way to obtain the fixed-bcritical values.

Statistical Mechanics Approximation of Biogeography-Based Optimization

2016 ◽  
Vol 24 (3) ◽  
pp. 427-458 ◽  
Author(s):  
Haiping Ma ◽  
Dan Simon ◽  
Minrui Fei
Keyword(s):  
Genetic Algorithm ◽  
Statistical Mechanics ◽  
Evolutionary Algorithm ◽  
Mathematical Description ◽  
Fitness Function ◽  
Statistical Properties ◽  
Separable Functions ◽  
The One ◽  
Good Agreement ◽  
Population Fitness

Biogeography-based optimization (BBO) is an evolutionary algorithm inspired by biogeography, which is the study of the migration of species between habitats. This paper derives a mathematical description of the dynamics of BBO based on ideas from statistical mechanics. Rather than trying to exactly predict the evolution of the population, statistical mechanics methods describe the evolution of statistical properties of the population fitness. This paper uses the one-max problem, which has only one optimum and whose fitness function is the number of 1s in a binary string, to derive equations that predict the statistical properties of BBO each generation in terms of those of the previous generation. These equations reveal the effect of migration and mutation on the population fitness dynamics of BBO. The results obtained in this paper are similar to those for the simple genetic algorithm with selection and mutation. The paper also derives equations for the population fitness dynamics of general separable functions, and we find that the results obtained for separable functions are the same as those for the one-max problem. The statistical mechanics theory of BBO is shown to be in good agreement with simulation.

scholarly journals Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jun Wang ◽  
Bi-hua Zhou ◽  
Shu-dao Zhou ◽  
Zheng Sheng
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Fitness Function ◽  
Chaotic Time Series ◽  
Genetic Operators ◽  
Genetic Simulated Annealing Algorithm ◽  
Function Expression ◽  
Annealing Operation

The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

scholarly journals Examining GDP Growth and Its Volatility: An Episodic Approach

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 890
Author(s):  
Jakub Bartak ◽  
Łukasz Jabłoński ◽  
Agnieszka Jastrzębska
Keyword(s):  
Economic Growth ◽  
Genetic Algorithm ◽  
Time Series ◽  
Human Capital ◽  
Growth Rates ◽  
Structural Breaks ◽  
Negative Relationship ◽  
Gdp Per Capita ◽  
Gdp Growth ◽  
Per Capita

In this paper, we study economic growth and its volatility from an episodic perspective. We first demonstrate the ability of the genetic algorithm to detect shifts in the volatility and levels of a given time series. Having shown that it works well, we then use it to detect structural breaks that segment the GDP per capita time series into episodes characterized by different means and volatility of growth rates. We further investigate whether a volatile economy is likely to grow more slowly and analyze the determinants of high/low growth with high/low volatility patterns. The main results indicate a negative relationship between volatility and growth. Moreover, the results suggest that international trade simultaneously promotes growth and increases volatility, human capital promotes growth and stability, and financial development reduces volatility and negatively correlates with growth.

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