Fuzzy Time Series Prediction Research Articles

A Fuzzy C-Means Clustering-Based Hybrid Multivariate Time Series Prediction Framework With Feature Selection

A Fuzzy C-Means Clustering-Based Hybrid Multivariate Time Series Prediction Framework With Feature Selection

Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics

Social networks and official statistics have become vital sources of information in times of health emergencies. The ability to monitor and profile social sentiment is essential for understanding public perception and response in the context of public health crises, such as the one resulting from the COVID-19 pandemic. This study will explore how social sentiment monitoring and profiling can be conducted using information from social networks and official statistics, and how this combination of data can offer a more complete picture of social dynamics in times of emergency, providing a valuable tool for understanding public perception and guiding a public health response. To this end, a three-layer architecture based on Big Data and Artificial Intelligence is presented: the first layer focuses mainly on collecting, storing, and governing the necessary data such as social media and official statistics; in the second layer, the representation models and machine learning necessary for knowledge generation are built, and in the third layer the previously generated knowledge is adapted for better understanding by crisis managers through visualization techniques among others. Based on this architecture, a KDD (Knowledge Discovery in Databases) framework is implemented using methodological tools such as sentiment analysis, fuzzy 2-tuple linguistic models and time series prediction with the Prophet model. As a practical demonstration of the proposed model, we use tweets as data source (from the social network X, formerly known as Twitter) generated during the COVID-19 pandemic lockdown period in Spain, which are processed to identify the overall sentiment using sentiment analysis techniques and fuzzy linguistic variables, and combined with official statistical indicators for prediction, visualizing the results through dashboards.

A hybrid electricity load prediction system based on weighted fuzzy time series and multi-objective differential evolution

With advances in science and technology, the demand for electricity is increasing dramatically. Consequently, reliable short-term power load prediction is critical to ensure the safety, efficiency, and cost-effectiveness of electricity grids. However, owing to the volatility and randomness of power load time series, traditional forecasting models have struggled to meet the current power system requirements for load prediction precision and stability. In light of this, this study proposes a new hybrid short-term electricity load prediction system that combines the variational modal decomposition (VMD) technique, multi-objective differential evolutionary algorithm (MODE), and weighted fuzzy time series (WFTS) prediction model to increase load prediction precision and stability. Four Belgian datasets (spring, summer, autumn, and winter) were used for short-term power load prediction experiments and were compared with prevalent prediction methods. The experimental results indicate that the designed system obtains MAPESpring=0.6670%, MAPESummer=0.5835%, MAPEAutumn=0.7049%, MAPEWinter=0.7123% and STDSpring=60.2575, STDSpring=62.6573, STDSpring=72.7393, STDSpring=71.5549, which are obviously superior to the comparison models in terms of prediction accuracy and stability.

A fuzzy time series forecasting model with both accuracy and interpretability is used to forecast wind power

Considering the current research focus on the interpretability and efficiency of wind speed prediction models, this research presents a novel prediction model for dynamic non-stationary fuzzy time series. The proposed model aims to effectively improve prediction accuracy and address the issues of low interpretability and excessive data preprocessing. Unlike existing mainstream hybrid systems for wind speed prediction, this model provides detailed explanations for almost every prediction step and eliminates the need for cumbersome data preprocessing steps. To improve the prediction accuracy of the proposed model, this research incorporates non-stationary sets to overcome the limitations of fuzzy time series in adapting to long-term changes. The developed algorithm, SFTSM, dynamically adjusts the fuzzy time series prediction to effectively address long-term prediction challenges. Furthermore, this study introduces an enhanced version of the artificial hummingbird algorithm, called SLG-AHA, to further improve the accuracy and stability of fuzzy time series prediction. Experimental results using data from the Shandong Penglai wind farm in China validate the effectiveness of the proposed model by showcasing its superior prediction accuracy and stability.

Fish swarmed Fuzzy Time Series for Photovoltaic’s Forecasting in Microgrid

Forecasting irradiation and temperature is important for designing photovoltaic systems because these two factors have a significant impact on system performance. Irradiation refers to the amount of solar radiation that reaches the earth's surface, and directly affects the amount of energy that can be generated by a photovoltaic system. Therefore, accurate irradiation forecasting is essential for estimating the amount of energy a photovoltaic system can produce, and can assist in determining the appropriate system size, configuration, and orientation to maximize energy output. Temperature also plays an important role in the performance of a photovoltaic system. With increasing temperature, the efficiency of the solar cell decreases, which means that the energy output of the system also decreases. Therefore, accurate temperature forecasts are essential for estimating system energy output, selecting suitable materials, and designing effective cooling systems to prevent overheating. In summary, forecasting irradiation and temperature is important for designing photovoltaic systems as it helps in determining suitable system size, configuration, orientation, material selection, and cooling system, which ultimately results in higher energy output and better system performance. In recent decades, many forecasting models have been built on the idea of fuzzy time series. There are several forecasting models proposed by integrating fuzzy time series with heuristic or evolutionary algorithms such as genetic algorithms, but the results are not satisfactory. To improve forecasting accuracy, a new hybrid forecasting model combines fish swarm optimization algorithm with fuzzy time series. The results of irradiance prediction / forecasting with the smallest error are using the type of Fuzzy Time Series prediction model optimized with FSOA with RMSE is 0.83832.

A novel intuitionistic fuzzy time series prediction model with cascaded structure for financial time series

Financial time series prediction problems, for decision-makers, are always crucial as they have a wide range of applications in the public and private sectors. This study presents a cascaded intuitionistic fuzzy model for financial time series prediction. The proposed prediction model has the ability to jointly and simultaneously model linear and nonlinear relationships in financial time series. Thus, it can adapt itself to both linear and non-linear surfaces of the data and can produce satisfactory predictions for financial time series. Moreover, the other reason why to be produced better predictions, the proposed model reckons non-membership degrees in addition to membership degrees in the prediction process. With these aspects, the proposed prediction model is different and superior to all models in the literature. This superiority has been proven by the analysis of 48 different financial time series containing TAIEX, DIJ, SSEC, and IEX data sets. The results have been evaluated in terms of RMSE, MAPE, and MdRAE metrics and some other perspectives as well. The proposed prediction model has achieved progress in prediction performance, up to 80% for TAIEX 2000–2004 datasets, 60% for TAIEX 2008–2018 datasets, approximately 50% for DJI and SSEC, and up to 70% for IEX. All the discussed indicators demonstrated the outstanding prediction performance of the proposed cascaded intuitionistic prediction model compared to some other state-of-the-art prediction tools.

Interval type-2 fuzzy C-means forecasting model for fuzzy time series

The fuzzy time-series (FTS) prediction model is based on fuzzy theory, which handles uncertain and fuzzy data. Owing to its good interpretability and prediction accuracy, the model based on the fuzzy C-means (FCM) clustering algorithm is most widely used. However, the fuzzy parameter m in the FCM algorithm is an empirical value that leads to uncertainty in classification results, which can negatively affect predictions. This study proposes a new model, IT2-FCM-FTS, that uses the interval type-2 (IT2) FCM algorithm instead of the traditional FCM to divide the sample domain and improve the performance of the FTS model. To verify the reliability of the proposed model, five datasets and four evaluation indices are used to compare the prediction results of the proposed model with the traditional ARIMA model and the one based on FCM. The results show that the proposed model is superior to both in terms of prediction accuracy.

Support Vector Machine and Granular Computing Based Time Series Volatility Prediction

With the development of information technology, a large amount of time-series data is generated and stored in the field of economic management, and the potential and valuable knowledge and information in the data can be mined to support management and decision-making activities by using data mining algorithms. In this paper, three different time-series information granulation methods are proposed for time-series information granulation from both time axis and theoretical domain: time-series time-axis information granulation method based on fluctuation point and time-series time-axis information granulation method based on cloud model and fuzzy time-series prediction method based on theoretical domain information granulation. At the same time, the granulation idea of grain computing is introduced into time-series analysis, and the original high-dimensional time series is granulated into low-dimensional grain time series by information granulation of time series, and the constructed information grains can portray and reflect the structural characteristics of the original time-series data, to realize efficient dimensionality reduction and lay the foundation for the subsequent data mining work. Finally, the grains of the decision tree are analyzed, and different support vector machine classifiers corresponding to each grain are designed to construct a global multiclassification model.

A hybrid sigma-pi neural network for combined intuitionistic fuzzy time series prediction model

A hybrid sigma-pi neural network for combined intuitionistic fuzzy time series prediction model

Predictive intelligence using ANFIS‐induced OWAWA for complex stock market prediction

Traditional time series prediction methods are unable to handle the complex nonlinear relationship of a large data set. Most of the existing techniques are unable to manage multiple dimensions of a data set, due to which the computational complexity escalates with the increasing size of a data set. Many machine learning (ML) methods are unable to handle known unknown predictions. This paper presents a new forecasting method in the neural network structure based on the induced ordered weighted average (IOWA) weighted average (WA) and fuzzy time series. The proposed model is more efficient than existing complexity handling fuzzy time series prediction methods and other traditional time series prediction methods. The proposed model can accommodate the IOWA operator, weighted average, and relevance degree of each concept in a particular problem for a fuzzy nonlinear prediction. The contribution of this paper is twofold. First, it contributes to theory by proposing a new IOWAWA layer in the neural network to handle complex nonlinear prediction for a large data set. The second contribution is the application of the approach to predict nonlinear stock market data. The robustness of the approach is tested using Australian Securities Exchange (ASX) stock data by considering a case study of the housing and property sector. We further compare the prediction accuracy of the approach with sixteen existing methods. The experimental results demonstrate that the proposed model outperforms existing methods.

A new QoS prediction model using hybrid IOWA-ANFIS with fuzzy C-means, subtractive clustering and grid partitioning

Quality of Service (QoS) is one of the key indicators to measure the overall performance of cloud services. The quantitative measurement of the QoS enables the service provider to manage its Service Level Agreement (SLA) in a viable way. It also supports a consumer in service selection and allows measuring the received services to comply with agreed services. There is much existing literature that tries to predict the QoS and assist stakeholders in their decision-making process. However, it is tricky to deal with multidimensional data in time series prediction methods. The computational complexity increases with an increase in data dimension, and it is a challenging task to give precise weights to each time interval. Existing prediction methods could not deal with the intricate reordering of input weights. To address this problem, we propose a novel Clustered Induced Ordered Weighted Averaging (IOWA) Adaptive Neuro-Fuzzy Inference System (ANFIS), (CI-ANFIS) model. This fuzzy time series prediction model reduces data dimension and handles the nonlinear relationship of the cloud QoS dataset. The proposed method uses an intelligent sorting mechanism that regulates uncertainty in prediction while incorporating a fuzzy neural network structure for optimal prediction results. The proposed method employs the IOWA operator to sort input arguments based on associated order-inducing variables and assign customised weights accordingly. The inputs are further classified using three fuzzy clustering methods - fuzzy c-means (FCM), subtractive clustering and grid partitioning. The inputs further pass to the ANFIS structure that takes the benefits of both the fuzzy and neural networks. The fuzzy structure in ANFIS builds understandable rules for cloud stakeholders and deals with uncertain occurrences of data. The model uses a real cloud QoS dataset extracted from the Amazon Elastic Compute Cloud (EC2) US-West instance and predict its behaviour every five minutes for the next 24 h. The proposed method is further compared with the existing twelve methods. The comparative results show that the proposed CI-ANFIS model outperforms all current techniques. The proposed approach opens a new area of research in various complex prediction problems such as stock trading, big data, complex IoT sensors, and other social computing problems.

Prediction model of burn-through point with fuzzy time series for iron ore sintering process

Burn-through point (BTP) is an essential parameter in the iron ore sintering process. Operators usually judge whether the current production is stable by monitoring the BTP. It comes with significant application prospects to predict the BTP accurately. A prediction model of the BTP with fuzzy time series is designed in this paper. First, the fuzzy time series prediction method with the Fuzzy C-Means clustering is presented as the core modeling method. A prediction model of the response is constructed to obtain a timely response to the current BTP. The prediction model of the difference is established to estimate the present unmeasurable disturbance on the BTP. Then, a hybrid prediction model is built, which realizes the composition of these two models by an adjustment factor. Finally, a series of experiments is carried out using the raw time series data from an iron and steel plant. The experimental result shows that the designed model has better prediction performance for the BTP than existing models, which is an advantage resulting from the hybrid structure and the fuzzy time series prediction model with the Fuzzy C-Means clustering. This prediction model of the BTP implies the foundation for the stable control of the iron ore sintering process.

Fuzzy time-series prediction model based on text features and network features

The prediction of time-series data is a challenging and complex issue. For many practical applications, network topology information and text information can play a perfect role in time-series prediction. This article takes stock data as an example by constructing a graph, connecting each stock’s upstream and downstream industries, and obtaining useful text features and topological features to predict the stock time-series. Based on the time-series data features, text features, and the topological features of the stock industry chain of machine learning, we compared our prediction model with other fuzzy time-series prediction methods, which are only based on historical features. The experiment shows that our method is better than the other methods in terms of the performance of multiple stocks in each stock’s time-series prediction. The experimental results show that the stock topology based on the industrial chain effectively improved time-series forecasting accuracy.

Combined Fuzzy Time Series Prediction Method for Fault Prediction of EML Pulse Capacitors

Metallized film capacitors are an important part of the electromagnetic rail launch system. It is of great significance to predict its failure. In engineering, the failure law of capacitor is obtained by cyclic pulse accelerated life test of capacitor module with a small number of samples, and is used to predict the actual failure of capacitor in electromagnetic launch (EML) system. This method has the following problems: first, there are few samples of capacitor in life test at affordable cost, which cannot include all capacitor characteristics; second, the time interval of actual EML missions and the voltage of the capacitors are both changing and inconsistent with the conditions used in the life test; third, the statistical law is affected by the measurement and other errors. For these reasons, the prediction accuracy of the existing methods is limited. In this article, a new combined time series prediction method is proposed by using the high-order fuzzy time series prediction theory. Based on the capacitor used in the prototype of an electromagnetic railgun, the life test data of 20 pulse capacitors are used as the prior knowledge input, and the prediction model is obtained. Five groups of capacitor life test data simulating real launch conditions are used as test data to verify that the prediction accuracy of the proposed method is better than that of the traditional method, and the prediction accuracy of capacitor capacitance, which made by different processes, is much higher than that of the traditional method.

Adaptive particle swarm optimized fuzzy algorithm to predict water table elevation

This study helps to select the length for fuzzy sets in fuzzy time series prediction. In order to examine the effect of intervals and evaluate the efficiency of the proposed algorithm, numerical data of water recharge and discharge are considered to predict water table elevation fluctuation (WTEF). Particle swarm optimization (PSO) is an influential tool to handle optimization of multi-model problems, whereas fuzzy logic can handle uncertainty. In this paper, adaptive inertia weights are adopted rather than static inertia weights for PSO, which further improves efficiency of PSO. This modified PSO is termed as adaptive particle swarm optimization (APSO). APSO optimizes the intervals and these intervals are further used to generate fuzzy sets for prediction. The results indicate that the APSO performs better than PSO and genetic algorithm approaches for the same problem.

Fuzzy Supervised Multi-Period Time Series Forecasting

Abstract The goal of this paper is to propose a new method for fuzzy forecasting of time series with supervised learning and k-order fuzzy relationships. In the training phase based on k previous historical periods, a multidimensional matrix of fuzzy dependencies is constructed. During the test stage, the fitted fuzzy model is run for validating the observations and each output value is predicted by using a fuzzy input vector of k previous intervals. The proposed algorithm is verified by a benchmark dataset for fuzzy time series forecasting. The results obtained are similar or better than those of other fuzzy time series prediction methods. Comparative analysis shows the high potential of the new algorithm as an alternative to fuzzy prediction and reveals some opportunities for its further improvement.

Fuzzy Time Series Forecasting Model based on Frequency Density and Similarity Measure Approach

This paper proposes an enhanced fuzzy time series (FTS) prediction model that can keep some information under a various level of confidence throughout the forecasting procedure. The forecasting accuracy is developed based on the similarity between the fuzzified historical data and the fuzzy forecast values. No defuzzification process involves in the proposed method. The frequency density method is used to partition the interval, and the area and height type of similarity measure is utilized to get the forecasting accuracy. The proposed model is applied in a numerical example of the unemployment rate in Malaysia. The results show that on average 96.9% of the forecast values are similar to the historical data. The forecasting error based on the distance of the similarity measure is 0.031. The forecasting accuracy can be obtained directly from the forecast values of trapezoidal fuzzy numbers form without experiencing the defuzzification procedure.

An algorithm of Saxena-Easo on fuzzy time series forecasting

This paper presents a forecast model of Saxena-Easo fuzzy time series prediction to study the prediction of Indonesia inflation rate in 1970-2016. We use MATLAB software to compute this method. The algorithm of Saxena-Easo fuzzy time series doesn’t need stationarity like conventional forecasting method, capable of dealing with the value of time series which are linguistic and has the advantage of reducing the calculation, time and simplifying the calculation process. Generally it’s focus on percentage change as the universe discourse, interval partition and defuzzification. The result indicate that between the actual data and the forecast data are close enough with Root Mean Square Error (RMSE) = 1.5289.

A Quantum based Evolutionary Algorithm for Stock Index and Bitcoin Price Forecasting

Quantum computing has emerged as a new dimension with various applications in different fields like robotic, cryptography, uncertainty modeling etc. On the other hand, nature inspired techniques are playing vital role in solving complex problems through evolutionary approach. While evolutionary approaches are good to solve stochastic problems in unbounded search space, predicting uncertain and ambiguous problems in real life is of immense importance. With improved forecasting accuracy many unforeseen events can be managed well. In this paper a novel algorithm for Fuzzy Time Series (FTS) prediction by using Quantum concepts is proposed in this paper. Quantum Evolutionary Algorithm (QEA) is used along with fuzzy logic for prediction of time series data. QEA is applied on interval lengths for finding out optimized lengths of intervals producing best forecasting accuracy. The algorithm is applied for forecasting Taiwan Futures Exchange (TIAFEX) index as well as for Bitcoin crypto currency time series data as a new approach. Model results were compared with many preceding algorithms.

A combined robust fuzzy time series method for prediction of time series

In case of outlier(s) it is inevitable that the performance of the fuzzy time series prediction methods is influenced adversely. Therefore, current prediction methods will not be able to provide satisfactory accuracy rates for defuzzified outputs (predictions) when the data has outlier(s). In this study, not only to be able to sort out this problem but also to be able to improve the forecasting accuracy, we propose a combined robust approach for fuzzy time series by assessing how the prediction performance of the methods will be affected from the outlier(s). In the proposed model, different from the current models, both crisp values and membership values are used as inputs and also real time series observations are taken as outputs. The proposed model therefore does not require defuzzification transaction and uses single multiplicative neuron model to determine the fuzzy relations and a robust fitness function in its training process. While performing the training process of this model by particle swarm optimization within a combined single optimization process, using crisps values and membership values together provides successful results by getting further information. The various implementations are illustrated to show that the proposed model could obtain more accurate and robust results in forecasting.an example is illustrated to show that the proposed method could obtain more accurate and robust results in forecasting.an example is illustrated to show that the proposed method could obtain more accurate and robust results in forecasting.an example is illustrated to show that the proposed method could obtain more accurate and robust results in forecasting.