Time Series Prediction Based on Gene Expression Programming

Zuo, Jian; Tang, Changjie; Li, Chuan; Yuan, Changan; An-long, Chen

doi:10.1007/978-3-540-27772-9_7

Cited by 69 publications

(24 citation statements)

References 3 publications

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“…More operators can be used in the GEP based model evolution. For example, the authors in [12] applied the differential operator for building the prediction model, and got desirable prediction results on sunspot series. We believe that more accurate prediction models can be evolved by introducing more complex operators.…”

Section: Discussionmentioning

confidence: 99%

“…(c) For time series prediction, GEP can generate models to account for trends and changes. Previous studies on GEP based time series prediction have produced very good results [8,9,10,11,12].…”

Section: Accepted M Manuscriptmentioning

confidence: 99%

“…GEP has been used successfully to solve various time series problems so far. Besides Ferreira's work in [7], the authors in [12] design a GEPbased method, called Differential by Microscope Interpolation, for sunspot series prediction. In [9], the authors apply an adaptive GEP-based method to predict the precipitation and temperatures in a region of Romania.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

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Mining effective multi-segment sliding window for pathogen incidence rate prediction

Duan

Tang

et al. 2013

Data & Knowledge Engineering

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Pathogen incidence rate prediction, which can be considered as time series modeling, is an important task for infectious disease incidence rate prediction and for public health. This paper investigates applying a genetic computation technique, namely GEP, for pathogen incidence rate prediction. To overcome the shortcomings of traditional sliding windows in GEP based time series modeling, the paper introduces the problem of mining effective sliding window, for discovering optimal sliding windows for building accurate prediction models. To utilize the periodical characteristic of pathogen incidence rates, a multi-segment sliding window consisting of several segments from different periodical intervals is proposed and used. Since the number of such candidate windows is still very large, a heuristic method is designed for enumerating the candidate effective multi-segment sliding windows. Moreover, methods to find the optimal sliding window and then produce a mathematical model based on that window are proposed. A performance study on real-world datasets shows that the techniques are effective and efficient for pathogen incidence rate prediction.

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Section: Discussionmentioning

confidence: 99%

Section: Accepted M Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

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Mining effective multi-segment sliding window for pathogen incidence rate prediction

Duan

Tang

et al. 2013

Data & Knowledge Engineering

Self Cite

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“…But the difference between model found by ECSID and original model becomes large when the model becomes complex. GEP-SWPM is proposed in [30], and the prediction is based on several generations of data before, so it is seriously affected by the noise. A new methods of GEP was proposed in [13,31], which is very effective for identifying parameter functions.…”

Section: Inverse Problems For Ordinary Differential Equationsmentioning

confidence: 99%

Improved gene expression programming to solve the inverse problem for ordinary differential equations

Chen

et al. 2018

Swarm and Evolutionary Computation

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Many complex systems in the real world evolve with time. These dynamic systems are often modeled by ordinary differential equations in mathematics. The inverse problem of ordinary differential equations is to convert the observed data of a physical system into a mathematical model in terms of ordinary differential equations. Then the model may be used to predict the future behavior of the physical system being modeled. Genetic programming has been taken as a solver of this inverse problem. Similar to genetic programming, gene expression programming could do the same job since it has a similar ability of establishing the model of ordinary differential systems. Nevertheless, such research is seldom studied before. This paper is one of the first attempts to apply gene expression programming for solving the inverse problem of ordinary differential equations. Based on a statistic observation of traditional gene expression programming, an improvement is made in our algorithm, that is, genetic operators should act more often on the dominant part of genes than on the recessive part. This may help maintain population diversity and also speed up the convergence of the algorithm. Experiments show that this improved algorithm performs much better than genetic programming and traditional gene expression programming in terms of running time and prediction precision.

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“…Galvan et al [8] apply their method to the Mackey-Glass and Venice lagoon time series. Further works have applied Evolutionary Algorithms [6,11], as gene expression programming [30] and cardinal B-Spline models [27] to the Time Series prediction area.…”

Section: Introductionmentioning

confidence: 99%

Time series prediction evolving Voronoi regions

2009

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Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able to find predictable local behaviors, and thus, attaining a better level of total prediction. This new method is based on a division of the input space into Voronoi regions by means of Evolution Strategies. Our method has been tested using different time series domains. One of them that represents the water demand in a water tank, through a long period of time. The other two domains are well known examples of chaotic time series (Mackey-Glass) and natural phenomenon time series (Sunspot). Results prove that, in most of cases, the proposed algorithm obtain better results than other algorithms commonly used.

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Time Series Prediction Based on Gene Expression Programming

Cited by 69 publications

References 3 publications

Mining effective multi-segment sliding window for pathogen incidence rate prediction

Mining effective multi-segment sliding window for pathogen incidence rate prediction

Improved gene expression programming to solve the inverse problem for ordinary differential equations

Time series prediction evolving Voronoi regions

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