發(fā)布時(shí)間：2018-10-26 17:09

【摘要】：由于時(shí)間序列預(yù)測能夠?yàn)槿藗兲峁┝己玫臎Q策支持,使其在眾多領(lǐng)域得到廣泛應(yīng)用。為了能夠?qū)��?shù)據(jù)不完整和含糊的情況進(jìn)行預(yù)測,模糊時(shí)間序列被提出。隨著數(shù)據(jù)時(shí)代的到來,時(shí)間序列和模糊時(shí)間序列模型受到了越來越多的關(guān)注。本文在對模糊時(shí)間序列和時(shí)間序列預(yù)測模型的研究基礎(chǔ)之上,得到了一些新的結(jié)果和研究方法,主要包括如下幾個(gè)方面的工作：1.隨著信息化的深入發(fā)展,過分強(qiáng)調(diào)精確性而可解釋性較差的預(yù)測模型已經(jīng)不能完全滿足時(shí)間序列預(yù)測的實(shí)際應(yīng)用需求。這就迫切需要提出具有高準(zhǔn)確率的同時(shí)還具有可解釋性的時(shí)間序列預(yù)測模型。針對上述問題,提出基于自動聚類和公理模糊集的模糊時(shí)間序列預(yù)測模型。該模型利用自動聚類算法根據(jù)樣本的分布情況產(chǎn)生不同長度的劃分區(qū)間,克服了靜態(tài)區(qū)間長度的缺點(diǎn)。并利用AFS分類器產(chǎn)生模糊趨勢的語義解釋,使得預(yù)測模型更容易被人理解。在預(yù)測的過程中能夠得到模糊趨勢,這為決策者提供了可靠的依據(jù)。然后,將模糊時(shí)間序列和經(jīng)典時(shí)間序列分析結(jié)合,提出一個(gè)基于趨勢預(yù)測和自回歸模型的模糊時(shí)間序列預(yù)測模型。該模型能夠挖掘時(shí)間序列中顯著的變化趨勢,并利用AR(2)模型確定預(yù)測數(shù)據(jù)的波動量,從而得到最終的預(yù)測值。將提出的兩個(gè)模糊時(shí)間序列預(yù)測模型分別應(yīng)用到現(xiàn)實(shí)中的時(shí)間序列上,并將實(shí)驗(yàn)結(jié)果與其他同類預(yù)測模型進(jìn)行比較,得到了較好的預(yù)測結(jié)果。2.分別結(jié)合模糊數(shù)據(jù)挖掘和模糊聚類提出兩個(gè)單步時(shí)間序列預(yù)測模型。在第一個(gè)模型中,根據(jù)越新發(fā)生與現(xiàn)在的關(guān)系越密切的原則,利用仿射傳播算法對子序列進(jìn)行聚類,從而確定最后的子序列所屬的類別,即找到與預(yù)測樣本關(guān)系最密切的子序列類。在此基礎(chǔ)上,利用模糊數(shù)據(jù)挖掘技術(shù)產(chǎn)生語義規(guī)則,并將得到的規(guī)則用于預(yù)測,這使得預(yù)測過程更透明更容易被人們理解。在第二個(gè)模型中,結(jié)合模糊聚類提出一個(gè)新的時(shí)間序列預(yù)測模型。首先,為了克服傳統(tǒng)聚類算法對數(shù)據(jù)維度的限制,同時(shí)能更準(zhǔn)確地度量時(shí)間序列之間的相似性,提出基于動態(tài)彎曲的模糊C-均值聚類算法。然后,利用此算法對構(gòu)造的時(shí)間序列數(shù)據(jù)進(jìn)行聚類,并根據(jù)聚類結(jié)果實(shí)施預(yù)測。所提出的兩個(gè)單步時(shí)間序列預(yù)測模型都被應(yīng)用到臺灣股指時(shí)間序列上,實(shí)驗(yàn)結(jié)果表明了模型的有效性,并得到比同類模型更好的預(yù)測結(jié)果。3.隨著人們研究的深入,多步預(yù)測比單步預(yù)測有著更重要的理論和實(shí)用價(jià)值�；�于信息粒和模糊聚類提出一個(gè)多步(長期)時(shí)間序列預(yù)測模型。信息粒化將時(shí)間序列分割(抽象)成若干有意義可操控的信息粒,這使得時(shí)間序列以更容易理解的方式呈現(xiàn)。因此,利用信息粒化構(gòu)造時(shí)間序列預(yù)測模型,使得預(yù)測模型具有可解釋性。由于預(yù)測模型是多步預(yù)測,一次可以預(yù)測出多個(gè)預(yù)測值,不需要反復(fù)迭代,大大減少了計(jì)算時(shí)間。以人工合成時(shí)間序列為例展示了預(yù)測模型的應(yīng)用過程,以此驗(yàn)證了模型的可行性。將該模型應(yīng)用到多組真實(shí)的時(shí)間序列上,實(shí)驗(yàn)結(jié)果顯示出了該模型的優(yōu)越性。
[Abstract]:Because the time series prediction can provide good decision support for people, it has been widely used in many fields. In order to be able to predict incomplete and ambiguous data, a fuzzy time series is proposed. With the advent of data age, time series and fuzzy time series model have been paid more and more attention. Based on the research of fuzzy time series and time series prediction model, some new results and research methods are obtained, including the following aspects: 1. With the in-depth development of information, too much emphasis is placed on the accuracy and the poor prediction model can not completely meet the actual application needs of time series prediction. There is an urgent need to propose a temporal sequence prediction model with high accuracy and an interpretable temporal sequence. In view of the above problems, a fuzzy time series prediction model based on automatic clustering and axiomatic fuzzy sets is proposed. The model utilizes the automatic clustering algorithm to generate different length division intervals according to the distribution of the samples, and overcomes the defect of the length of the static interval. The semantic interpretation of fuzzy tendency is generated by AFS classifier, which makes the prediction model easier to understand. Fuzzy trends can be obtained in the prediction process, which provides a reliable basis for decision makers. Then, combining fuzzy time series and classical time series analysis, a fuzzy time series prediction model based on trend prediction and autoregressive model is proposed. The model can dig a significant change trend in the time series, and use the AR (2) model to determine the fluctuation of the prediction data, so as to obtain the final forecast value. The two fuzzy time series prediction models are respectively applied to the real time series, and the experimental results are compared with other similar prediction models, and a better prediction result is obtained. Two single-step time series prediction models are proposed in combination with fuzzy data mining and fuzzy clustering. In the first model, according to the principle of closer relationship between the new occurrence and the present relationship, the sub-sequence of the last sub-sequence is determined by using the affine propagation algorithm so as to determine the category to which the last sub-sequence belongs, i.e. to find the sub-sequence class closest to the predicted sample relation. On this basis, the fuzzy data mining technology is used to generate semantic rules, and the obtained rules are used for prediction, which makes the prediction process more transparent and easier to understand. In the second model, a new time series prediction model is proposed in combination with fuzzy clustering. Firstly, in order to overcome the limitation of the traditional clustering algorithm on the data dimension, the similarity between the time series can be more accurately measured, and a dynamic bending-based fuzzy C-means clustering algorithm is proposed. Then, the time series data constructed by this algorithm is used to gather the data, and the prediction is carried out according to the result of the poly. Both single-step time series prediction models are applied to the Taiwan stock index time series. The experimental results show the validity of the model and get better prediction results than those of the same model. With the in-depth study, multi-step prediction has more important theoretical and practical value than single-step prediction. A multi-step (long-term) time series prediction model is proposed based on information particle and fuzzy clustering. Information Granularization divides the time sequence (abstracted) into a number of meaningful controllable information particles, which render the time series presented in a more understandable way. Therefore, using the information grain structure time series prediction model, the prediction model has interpretability. Because the prediction model is multi-step prediction, a plurality of prediction values can be predicted at one time, repeated iterations are not needed, and the calculation time is greatly reduced. In this paper, the application process of the prediction model is presented in a synthetic time series as an example, and the feasibility of the model is verified. The model is applied to several sets of real time series, and the experimental results show the superiority of the model.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：O211.61

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