Construction of symmetry triangular fuzzy number procedure (STFNP) using statistical information for autoregressive forecasting

Single-point data are used for data collection. However, data collected by various data collection methods are often exposed to uncertainties that may affect the information presented by the quantitative results. This also causes the forecast model developed to be less precise because of the uncertainties contained in the input data. It is essential to describe the uncertainty in data to obtain a realistic result from data analysis. However, most studies focus on model uncertainty regardless of data uncertainty. The data processing carried out may not always take care of uncertainty. When uncertainties in the raw data are not sufficiently handled, this creates more errors that are included in the predicted model. Standard procedures are also very limited to be followed in order to transform a single-point value into Triangular Fuzzy Number (TFN), which addresses the uncertainty. Thus, the data preparation procedure of Symmetry Triangular Fuzzy Number (STFN) is presented in this study to build an improved autoregressive model for time series forecasting. This study presents the proposed Symmetry Triangular Fuzzy Number Procedure (STFNP) using percentage error method and standard deviation method for first-order autoregressive forecasting. Percentage error rate method involves three different percentage rates, while the second method uses the standard deviation of the data. Simulations and verification procedures are presented and are accompanied with numerical examples using actual datasets of Air Pollutant Index and stock markets of selected ASEAN countries. This study reveals that the percentage error and standard deviation methods, which were used to construct the TFN, can achieve the same or better accuracy as compared to a single-point procedure. The results of the simulations and experiments show that the standard deviation method produces better results compared to the other proposed approaches and the conventional approach. Besides, the systematic procedure to construct the TFN does not deviate from single-point procedures. Importantly, uncertain data being treated avoids more uncertainties that would have been brought to the outcome of the forecast model and consequently improves prediction accuracy

Integration of ANFIS with PCA and DWT for daily suspended sediment concentration prediction

Quantifying sediment load is vital for aquatic and riverine biota and has been the subject of various environmental studies since sediment plays a key role in maintaining ecological integrity, river morphology and agricultural productivity. However, predicting sediment concentration in rivers is difficult because of the non-linear relationships of flow rates, geophysical characteristics and sediment loads. It is thus very important to propose suitable statistical methods which can provide fast, accurate and robust prediction of suspended sediment concentration (SSC) for management guidance. In this study, we developed coupled models of discrete wavelet transform (DWT) with adaptive neuro-fuzzy inference system (ANFIS), named DWT-ANFIS, and principal component analysis (PCA) with ANFIS, named PCA-ANFIS, for SSC time-series modeling. The coupled models and single ANFIS model were trained and tested using long-term daily SSC and river discharge which were measured on the Schuylkill and Iowa Rivers in the United States. The findingsshowed that the PCA-ANFIS performed better than the single ANFIS and the coupled DWT-ANFIS. Further applications of the PCA-ANFIS should be considered for simulation and prediction of other indicators relating to weather, water resources, and the environment &nbsp

Regression Driven F--Transform and Application to Smoothing of Financial Time Series

In this paper we propose to extend the definition of fuzzy transform in order to consider an interpolation of models that are richer than the standard fuzzy transform. We focus on polynomial models, linear in particular, although the approach can be easily applied to other classes of models. As an example of application, we consider the smoothing of time series in finance. A comparison with moving averages is performed using NIFTY 50 stock market index. Experimental results show that a regression driven fuzzy transform (RDFT) provides a smoothing approximation of time series, similar to moving average, but with a smaller delay. This is an important feature for finance and other application, where time plays a key role.Comment: IFSA-SCIS 2017, 5 pages, 6 figures, 1 tabl

A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation

The fast and accurate modelling of thermal errors in machining is an important aspect for the implementation of thermal error compensation. This paper presents a novel modelling approach for thermal error compensation on CNC machine tools. The method combines the Adaptive Neuro Fuzzy Inference System (ANFIS) and Grey system theory to predict thermal errors in machining. Instead of following a traditional approach, which utilises original data patterns to construct the ANFIS model, this paper proposes to exploit Accumulation Generation Operation (AGO) to simplify the modelling procedures. AGO, a basis of the Grey system theory, is used to uncover a development tendency so that the features and laws of integration hidden in the chaotic raw data can be sufficiently revealed. AGO properties make it easier for the proposed model to design and predict. According to the simulation results, the proposed model demonstrates stronger prediction power than standard ANFIS model only with minimum number of training samples

Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. Apart from introduction and references the paper is organized as follows. The section 2 presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learning-based algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches