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This paper evaluates the performance of four artificial intelligence algorithms for building energy consumption prediction. The backward propagation neural network (BPNN), support vector regression (SVR), adaptive network-based fuzzy inference system (ANFIS) and extreme learning machine (ELM) methods are reviewed and their performances for predicting building energy consumption are compared. A selection...
Nowadays, fault detect and prediction is quite important for the purpose of ensuring the correct functioning of complex system; nevertheless, it is usually difficult to establish an exact mathematical model in analytical form for complex system, therefore, fault prediction of complex system always relays on the analysis of the observed chaotic time series. In order to enhance the validity and accuracy...
This research work emphases on the prediction of future stock market index values based on historical data. The experimental evaluation is based on historical data of 10 years of two indices, namely, CNX Nifty and S&P Bombay Stock Exchange (BSE) Sensex from Indian stock markets. The predictions are made for 1–10, 15, 30, and 40 days in advance. This work proposes to combine the predictions/estimates...
Time series forecasting has a fundamental importance in various practical domains. Many models have been proposed in literature to model and predict the Time Series Data (TSD) efficiently. As the modeling and prediction depends on the nature of TSD, one model may not be opt for all applications. This paper presents a hybrid model based on Particle Swarm Optimization (PSO) with Least Square Support...
This paper presents an automated method for seizure detection in EEGs using an increment entropy (IncrEn) and support vector machines (SVMs). The IncrEn is a measure of the complexity of time series, which characterizes both the permutation of values and the temporal order of values. The IncrEn is used to extract features of epileptic EEGs and normal EEGs. The SVMs are employed to classify seizure...
Multivariate time series (MTS) exist in many applications. Due to all kinds of interference factors, missing data in MTS is inevitable. Aiming at this problem, a filling method based on least squares support vector machine (LSSVM) is proposed. Firstly, for the series containing missing data, similar series are searched, and its results are viewed as the training set. Secondly, to make use of the correlation...
In this paper, we address the problem of predicting wind turbine electrical subsystem fault using time series data obtained from multiple sensors on wind turbine. While considering this as a time series classification problem, we are facing with the challenge that there is no explicit label information regarding the temporal location and duration of symptoms of the fault. Besides, significant data...
In this paper, we propose an improvement to the method of combined segmentation verification for multi-script signature verification. In our previous paper, we proposed generalized segmentation verification (GSV) for multi-script signature verification and evaluated the method using the SigComp dataset. GSV improved the performance of multi-script signature verification by introducing a two-stage...
Efforts have been made in financial markets to deal with price movement predicting. Recent studies have shown that the market can be outperformed by methodologies with the aid of science. In other words, it has been shown that methods based on computational intelligence can be more profitable than a buy-and-hold strategy. This paper proposes a probabilistic and dynamic chart pattern recognition hybrid...
The exploitation of the high revisit time (8–16 days) by the COSMO-SkyMed® (CSK®) satellites is an important opportunity for agricultural mapping. This study aims at evaluating CSK® potentiality to classify different crop types, with CSK® multi-temporal images collected over the agricultural site of Marchfeld, in Austria. Two different time series of CSK® HIMAGE SAR scenes, at 3m resolution, 9 at...
Classifying sequential data is an important problem in machine learning with applications in time series, sensor streams, and image analysis. The ordered structure of sequential data presents a difficulty for the standard classification models, which has motivated the task of generating features for vector-based discriminative models. Shapelet methods, which have been extensively studied in this topic,...
Electroencephalographic (EEG) signals are produced in brain due to firing of the neurons. Any anomaly found in the EEG indicates abnormality associated with brain functioning. The efficacy of automated analysis of EEG depends on features chosen to represent the time series, classifier used and quality of training data. In this work, we present automated analysis of EEG time series acquired from two...
Accurate electricity price prediction is one of the most important parts of decision making for electricity market participants to make reasonable competing strategies. Support Vector Machine (SVM) is a novel algorithm based on a predictive modeling method and a powerful classification method in machine learning and data mining. Most of SVM-based and non-SVM-based models ignore other important factors...
Large amount of hydrological data set is a kind of big data, which has much hidden and potentially useful knowledge. Hydrological prediction is important for the state flood control and drought relief. How to forecast accurately and timely with hydrological big data becomes a big challenge. There are some forecasting techniques used widely. However, they are limited by their adaptability, the data...
This paper deals with online detection and accommodation of outliers in transient time series by appealing to a machine learning technique. The methodology is based on a Least Squares Support Vector Machine technique together with a sliding window-based learning algorithm. A modification to this method is proposed so as to extend its application to transient raw data collected from transmitters attached...
The recently introduced Support Vector Method (SVM) is one of the most powerful methods for training a Radial Basis Function (RBF) filter in a batch mode. This paper proposes a modification of this method for on-line adaptation of the filter parameters on a block-by-block basis. The proposed method requires a limited number of computations and compares well with other adaptive RBF filters.
One of the effective methodologies for time series classification is to identify informative subsequence patterns in time series and exploit them as discriminative features. Previous studies on this methodology have achieved promising results using a small number of individually selected patterns. However, there remain difficulties in finding a set of related patterns or patterns of a minor class,...
In this paper, for the first time, an ensemble of deep learning belief networks (DBN) is proposed for regression and time series forecasting. Another novel contribution is to aggregate the outputs from various DBNs by a support vector regression (SVR) model. We show the advantage of the proposed method on three electricity load demand datasets, one artificial time series dataset and three regression...
Signatures are the single most widely used method of identifying an individual but they carry with them an alarmingly significant number of vulnerabilities, implying the need for an effective and robust method of precisely identifying an individual's signature. The signature of an individual is visually acquired by using a pen-based tracking system [1], [2]. This paper considers the possibility of...
Machine Learning methods such as Neural Network (NN) and Support Vector Regression (SVR) have been studied extensively for time series forecasting. Multiple Kernel Learning (MKL) which utilizes SVR as the predictor is yet another recent approaches to choose suitable kernels from a given pool of kernels by means of a linear combination of some base kernels. However, some literatures suggest that this...
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