Temperature prediction using fuzzy time series

A drawback of traditional forecasting methods is that they can not deal with forecasting problems in which the historical data are represented by linguistic values. Using fuzzy time series to deal with forecasting problems can overcome this drawback. In this paper, we propose a new fuzzy time series model called the two-factors time-variant fuzzy time series model to deal with forecasting problems. Based on the proposed model, we develop two algorithms for temperature prediction. Both algorithms have the advantage of obtaining good forecasting results.     

Swarm-based translation-invariant morphological prediction method for financial time series forecasting

In this paper, we present a method to overcome the random walk (RW) dilemma for financial time series forecasting, called swarm-based translation-invariant morphological prediction (STMP) method. It consists of a hybrid model composed of a modular morphological neural network (MMNN) combined with a particle swarm optimizer (PSO), which searches for the best time lags to optimally describe the time series phenomenon, as well as estimates the initial (sub-optimal) parameters of the MMNN (weights, architecture and number of modules). An additional optimization is performed with each particle of the PSO population (a distinct MMNN) using the back-propagation (BP) algorithm. After the MMNN parameters adjustment, we use a behavioral statistical test and a phase fix procedure to adjust time phase distortions observed in financial time series. Finally, we conduct an experimental analysis with the proposed method using four real world stock market time series, where five well-known performance metrics and a fitness function are used to assess the prediction performance. The obtained results are compared with those generated by classical models presented in the literature.     

A generalized model for financial time series representation and prediction

Traditional financial analysis systems utilize low-level price data as their analytical basis. For example, a decision-making system for stock predictions regards raw price data as the training set for classifications or rule inductions. However, the financial market is a complex and dynamic system with noisy, non-stationary and chaotic data series. Raw price data are too random to characterize determinants in the market, preventing us from reliable predictions. On the other hand, high-level representation models which represent data on the basis of human knowledge of the problem domain can reduce the randomness in the raw data. In this paper, we present a high-level representation model easy to translate from low-level data into the machine representation. It is a generalized model in that it can accommodate multiple financial analytical techniques and intelligent trading systems. To demonstrate this, we further combine the representation with a probabilistic model for automatic stock trades and provide promising results. An erratum to this article can be found at     

基于二阶马尔可夫模型的模糊时间序列预测

针对当前模糊时间序列模型存在的缺乏有效论域划分方法和模糊关系前件多为一阶的现状,提出了基于二阶马尔可夫模型的模糊时间序列预测方法。应用模糊C均值聚类方法,获得序列中元素的隶属度;引入二阶马尔可夫模型中的转移概率矩阵表示模糊关系,更新了传统的模糊关系表示和运算;预测待求元素在各个模糊聚类的隶属度,并利用重心法去模糊化。将该模型运用到移动3G网络的性能预测中,与传统模糊时间序列预测方法相比,其准确性有了较大提高。     

Regularized least squares fuzzy support vector regression for financial time series forecasting

In this paper, we propose a novel approach, termed as regularized least squares fuzzy support vector regression, to handle financial time series forecasting. Two key problems in financial time series forecasting are noise and non-stationarity. Here, we assign a higher membership value to data samples that contain more relevant information, where relevance is related to recency in time. The approach requires only a single matrix inversion. For the linear case, the matrix order depends only on the dimension in which the data samples lie, and is independent of the number of samples. The efficacy of the proposed algorithm is demonstrated on financial datasets available in the public domain.     

Fuzzy prediction of chaotic time series based on fuzzy clustering

The main purpose of this paper is to study a new method to model and predict a chaotic time series using a fuzzy model. First, the GK fuzzy clustering method is used to confirm the input space of the fuzzy model. The goal is to divide the training patterns into representative groups so that patterns within one cluster are more similar than those belonging to other clusters. Then, the Kalman filtering algorithm with singular value decomposition is applied to estimate the consequent parameters of the fuzzy model in order to avoid error delivery and error accumulation. The effectiveness of the proposed method is evaluated through simulated examples, including Mackey‐Glass time series and Lorenz chaotic systems. The results show that the proposed method provides effective and accurate prediction. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Statistical fuzzy interval neural networks for currency exchange rate time series prediction

In this paper, the statistical fuzzy interval neural network with statistical interval input and output values is proposed to perform statistical fuzzy knowledge discovery and the currency exchange rate prediction. Time series data sets are grouped into time series data granules with statistical intervals. The statistical interval data sets including week-based averages, maximum errors of estimate and standard deviations are used to train the fuzzy interval neural network to discover fuzzy IF-THEN rules. The output of the fuzzy interval neural network is an interval value with certain percent confidence. Simulations are completed in terms of the exchange rates between US Dollar and other three currencies (Japanese Yen, British Pound and Hong Kong Dollar). The simulation results show that the fuzzy interval neural network can provide more tolerant prediction results.     

Grey relational grade in local support vector regression for financial time series prediction

Support vector regression (SVR) has often been applied in the prediction of financial time series with many characteristics. On account of much time consumption of global SVR, local machines are carried out to accelerate the computation. In this paper, we introduce local grey SVR (LG-SVR) integrated grey relational grade with local SVR for financial time series forecasting. Pattern search method and leave-one-out errors are adopted for model selection. Experimental results of three real financial time series prediction demonstrate that LG-SVR can speed up computing speed and improve prediction accuracy.     

A novel high-order weighted fuzzy time series model and its application in nonlinear time series prediction

Conventional time series forecast models can hardly develop the inherent rules of complex non-linear dynamic systems because the strict assumptions they need cannot always be met in reality, whereas fuzzy time series (FTS) techniques can be used even the records of times series have uncertainty and instability since they do not need strict assumptions. In previous study of FTS, the process of aggregating the past observations and assigning proper weights of fuzzy logical relationship groups are ignored, which may lead to poor forecasting accuracy since they are important aspects in time series prediction and analysis where determination of future trends depends only on past observations. In this paper, a novel high-order FTS model is constructed to make time series forecasting. Specifically, by applying the harmony search intelligence algorithm, the optimal lengths of intervals are tuned. Moreover, regularly increasing monotonic quantifiers are employed on fuzzy sets to obtain the weights of ordered weighted aggregation. Simultaneously, the weights of right-hand side of fuzzy logical relationship groups are explored to compensate the presence of bias in the prediction. In the part of empirical analysis, the developed model was applied to predict three well-known time series: numbers of enrollment of Alabama University, TAIEX and electricity load demand of New South Wales and the results obtained were compared with several counterparts, including some old and recently developed models. Experimental results demonstrate that the developed model cannot only achieve higher accuracy of prediction, but also capture the fuzzy features and characters.     

Efficient financial time series prediction with evolutionary virtual data position exploration

Prediction of stock index remains a challenging task of the financial time series prediction process. Random fluctuations in the stock index make it difficult to predict. Usually the time series prediction is based on the observations of past trend over a period of time. In general, the curve the time series data follows has a linear part and a non-linear part. Prediction of the linear part with past history is not a difficult task, but the prediction of non linear segments is difficult. Though different non-linear prediction models are in use, but their prediction accuracy does not improve beyond a certain level. It is observed that close enough data positions are more informative where as far away data positions mislead prediction of such non linear segments. Apart from the existing data positions, exploration of few more close enough data positions enhance the prediction accuracy of the non-linear segments significantly. In this study, an evolutionary virtual data position (EVDP) exploration method for financial time series is proposed. It uses multilayer perceptron and genetic algorithm to build this model. Performance of the proposed model is compared with three deterministic methods such as linear, Lagrange and Taylor interpolation as well as two stochastic methods such as Uniform and Gaussian method. Ten different stock indices from across the globe are used for this experiment and it is observed that in majority of the cases performance of the proposed EVDP exploration method is better. Some stylized facts exhibited by the financial time series are also documented.

     

Chaotic time series prediction via artificial neural square fuzzy inference system

The present article investigates the application of second order TSK (Takagi Sugeno Kang) fuzzy systems in predicting chaotic time series. A method has been introduced for training second order TSK fuzzy systems using ANFIS (Artificial Neural Fuzzy Inference System) training method. In a second order TSK system existence of nonlinear terms in the rules’ consequence prohibits use of current available ANFIS codes as is but the proposed method makes it possible to use ANFIS for a class of simplified second order TSK systems. The main impact of this method on the expert and intelligent systems is to provide a new way for modeling and predicting the future situation of more complex phenomena with a smaller decision rule base. The most significance of the proposed method is the simplicity and available code reuse property. As a case study the proposed method is used for the prediction of chaotic time series. Error comparison shows that the proposed method trains the second order TSK system more effectively.     

时间序列一步预测方法*

为了改善时间序列预测的性能,提出一种时间序列一步预测分析方法。首先将一个时间序列分解为总体趋势和个体波动两个序列,然后分别对这两个序列进行预测分析,再将结果合成得到最终的预测结果。对于总体趋势序列利用加权滤波算法进行分析,而对于个体波动序列则先进行混沌特性分析,再结合混沌预测分析方法对其进行预测。利用混沌优化方法动态地调节预测网络的参数,逐渐提高网络的预测精度。利用该方法分别对混沌序列、实际股票价格等序列进行了仿真预测分析,仿真结果表明,该方法具有良好的预测效果。     

Evolutional RBFNs prediction systems generation in the applications of financial time series data

An artificial neural prediction system is automatically developed with the combinations of step wise regression analysis (SRA), dynamic learning and recursive-based particle swarm optimization (RPSO) learning algorithms. In the first stage, the SRA can be considered like a data filtering machine to choose two primary factors from 20 channel technical indexes as input variables of the RBFNs system. Then, an efficient dynamic learning algorithm is applied to sequentially generate RBFs functions from training data set, where it can efficiently determine the proper number of RBFs’ centers and their associated positions. It can be exploited to forecast appropriate behaviors of the wanted identified financial time series data. While characteristics of training data set are automatically mined and generated by the proposed dynamic learning algorithm, architecture of the RBFNs prediction system is initially represented with collected information. Moreover, the RPSO learning scheme with the hybrid particle swarm optimization (PSO) and recursive least-squares (RLS) learning methods are applied to extract those appropriate parameters of the RBFNs prediction system.The RBFNs prediction systems are implemented in data analysis, module generation and price trend of the financial time series data. It not only automatically determines proper RBFs number but also fast approach the desired target in actual trading of Taiwan stock index (TAIEX). Computer simulations in training and testing phases of historic TAIEX are compared with other learning methods, which illustrate our great performance not only increases the accuracy of the stock price prediction but also improves the win rate in the trend of TAIEX.     

A disk-aware algorithm for time series motif discovery

Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding time series motifs exactly in massive databases is an open problem. Previous efforts either found approximate motifs or considered relatively small datasets residing in main memory. In this work, we leverage off previous work on pivot-based indexing to introduce a disk-aware algorithm to find time series motifs exactly in multi-gigabyte databases which contain on the order of tens of millions of time series. We have evaluated our algorithm on datasets from diverse areas including medicine, anthropology, computer networking and image processing and show that we can find interesting and meaningful motifs in datasets that are many orders of magnitude larger than anything considered before.     

Methodology for long-term prediction of time series

In this paper, a global methodology for the long-term prediction of time series is proposed. This methodology combines direct prediction strategy and sophisticated input selection criteria: k-nearest neighbors approximation method (k-NN), mutual information (MI) and nonparametric noise estimation (NNE). A global input selection strategy that combines forward selection, backward elimination (or pruning) and forward–backward selection is introduced. This methodology is used to optimize the three input selection criteria (k-NN, MI and NNE). The methodology is successfully applied to a real life benchmark: the Poland Electricity Load dataset.     

A spatial contagion measure for financial time series

A novel spatial contagion measure is proposed that is based on the calculation of suitable conditional Spearman’s correlations extracted from the financial time series of interest. Algorithms for the numerical estimation of this measure are illustrated, together with a simulation study showing its features in relations with popular families of copulas. Finally, two applications are presented about the use of spatial contagion measure for determining (asymmetric) linkages in the financial systems, and creating clusters of financial time series. In particular, contrarily to previous approaches in the literature, such clusters identify which time series increase their (positive) association when the market is under distress. The presented methodology is also expected to be useful to select a diversified portfolio of asset returns.     

Fuzzy wavelet neural network based on fuzzy clustering and gradient techniques for time series prediction

This paper presents the development of fuzzy wavelet neural network system for time series prediction that combines the advantages of fuzzy systems and wavelet neural network. The structure of fuzzy wavelet neural network (FWNN) is proposed, and its learning algorithm is derived. The proposed network is constructed on the base of a set of TSK fuzzy rules that includes a wavelet function in the consequent part of each rule. A fuzzy c-means clustering algorithm is implemented to generate the rules, that is the structure of FWNN prediction model, automatically, and the gradient-learning algorithm is used for parameter identification. The use of fuzzy c-means clustering algorithm with the gradient algorithm allows to improve convergence of learning algorithm. FWNN is used for modeling and prediction of complex time series and prediction of foreign-exchange rates. Exchange rates are dynamic process that changes every day and have high-order nonlinearity. The statistical data for the last 2 years are used for the development of FWNN prediction model. Effectiveness of the proposed system is evaluated with the results obtained from the simulation of FWNN-based systems and with the comparative simulation results of previous related models.     

The application of ridge polynomial neural network to multi-step ahead financial time series prediction

Motivated by the slow learning properties of multilayer perceptrons (MLPs) which utilize computationally intensive training algorithms, such as the backpropagation learning algorithm, and can get trapped in local minima, this work deals with ridge polynomial neural networks (RPNN), which maintain fast learning properties and powerful mapping capabilities of single layer high order neural networks. The RPNN is constructed from a number of increasing orders of Pi–Sigma units, which are used to capture the underlying patterns in financial time series signals and to predict future trends in the financial market. In particular, this paper systematically investigates a method of pre-processing the financial signals in order to reduce the influence of their trends. The performance of the networks is benchmarked against the performance of MLPs, functional link neural networks (FLNN), and Pi–Sigma neural networks (PSNN). Simulation results clearly demonstrate that RPNNs generate higher profit returns with fast convergence on various noisy financial signals.     

Knowledge discovery in time series databases

Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. In this correspondence, we introduce a general methodology for knowledge discovery in TSDB. The process of knowledge discovery in TSDR includes cleaning and filtering of time series data, identifying the most important predicting attributes, and extracting a set of association rules that can be used to predict the time series behavior in the future. Our method is based on signal processing techniques and the information-theoretic fuzzy approach to knowledge discovery. The computational theory of perception (CTP) is used to reduce the set of extracted rules by fuzzification and aggregation. We demonstrate our approach on two types of time series: stock-market data and weather data.     

An adaptive local linear optimized radial basis functional neural network model for financial time series prediction

For financial time series, the generation of error bars on the point of prediction is important in order to estimate the corresponding risk. In recent years, optimization techniques-driven artificial intelligence has been used to make time series approaches more systematic and improve forecasting performance. This paper presents a local linear radial basis functional neural network (LLRBFNN) model for classifying finance data from Yahoo Inc. The LLRBFNN model is learned by using the hybrid technique of backpropagation and recursive least square algorithm. The LLRBFNN model uses a local linear model in between the hidden layer and the output layer in contrast to the weights connected from hidden layer to output layer in typical neural network models. The obtained prediction result is compared with multilayer perceptron and radial basis functional neural network with the parameters being trained by gradient descent learning method. The proposed technique provides a lower mean squared error and thus can be considered as superior to other models. The technique is also tested on linear data, i.e., diabetic data, to confirm the validity of the result obtained from the experiment.