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Financial time series prediction is remains a challenge, due to the nonstationary and fuzziness financial data. In this paper, we propose and achieve a hybrid financial time series model by combining the Maximum Entropy (ME), Support Vector Regression (SVR) and Trend model based on Artificial neural networks (ANNs) for forecasting financial time series. The method contains three steps. The first step...
In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully...
This paper presents detailed anomaly detection evaluation on operational time-series data of Internet of Things (IoT) based household devices in general and Heating, Ventilation and Air Conditioning (HVAC) systems in specific. Due to the number of issues observed during evaluation of widely used distance-based, statistical-based, and cluster-based anomaly detection techniques, we also present a pattern-based...
Fault diagnosis of incipient crack failure in rotating shafts allows the detection and identification of performance degradation as early as possible in industrial plants, such as downtime and potential injury to personnel. The present work studies the performance and effectiveness of crack fault detection by means of applying wavelet packet decomposition (WPD) and empirical mode decomposition (EMD)...
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...
Fault prediction technology is important to avoid serious process failure. This paper is concerned with the fault prediction of dynamic industrial process with incipient faults and proposes a canonical variable trend analysis (CVTA) based fault prediction method. In the proposed method, canonical variate analysis (CVA) algorithm is firstly applied to analyze the process dynamics and extract the uncorrelated...
Polynomials have shown to be useful basis functions in the identification of nonlinear systems. However estimation of the unknown coefficients requires expensive algorithms, as for instance it occurs by applying an optimal least square approach. Bernstein polynomials have the property that the coefficients are the values of the function to be approximated at points in a fixed grid, thus avoiding a...
Gear is one of the most important components in rotary machine systems. The vibration signals generated from gearbox show strong nonlinearity or chaotic behavior. To identify the complex nonlinear behavior of gear faults, recurrence network is introduced to extract features of gear vibration under different conditions. Quantitative characteristics (such as mean degree centrality, global clustering...
Air quality forecasting is of great significance in environmental science, because air pollution has adverse influence on human beings and the environment. In this paper, a model W-SVM combining wavelet technique and support vector machine (SVM) is developed to forecast daily PM10 concentration. Firstly, time series of PM10 concentration is decomposed into several different frequencies by the static...
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...
Temporal sequences of images called Satellite Image Time Series (SITS) allow land cover monitoring and classification by affording a large amount of images. Many approaches attempt to exploit this multi-temporal data in order to extract relevant information such as classification-based techniques. In this paper we compare low and high levels classification-based approaches that aim to reveal the SITS...
Tme series polarimetric SAR image classification relies on learned understanding of how the set of pixels in an image relate by relative position and how the information of different dates in a time series change as time goes on. In this paper, we firstly integrate the incoherent information in the spatial scale and the coherent information in the temporal scale to form the feature for time series...
This work presents a multi-temporal and multi-source approach for glacier cover classification, i.e. bare soil, glacier ice, firn, and snow. The method is based on Hidden Markov Model (HMM) and Support Vector Machine (SVM) and can handle different kinds of satellite virtual constellations composed of high-resolution optical and/or SAR platforms. The proposed method is tested on a Sentinel-1 time series...
In this paper, we study the potential of the new satellite Sentinel-2 (S2) images to identify tree species in temperate forests. Fourteen tree species are classified from eleven S2 images acquired from winter 2015 to autumn 2016 with 2181 reference pixels. Two datasets are compared: (1) the 4-bands dataset including the 10-m VNIR images only and (2) the 10-bands dataset including the red-edge and...
This paper concerns extracting knowledge from limit order books and using it to predict significant changes in the price of the financial asset under study. Limit order books are encoded in a feature-based data representation. A binary Support Vector Machine classifier is trained to predict whether a particular limit order book leads to a significant change in the price in a few successive time instants,...
In the recent past, crime analyses are required to reveal the complexities in the crime dataset. This process will help the parties that involve in law enforcement in arresting offenders and directing the crime prevention strategies. The ability to predict the future crimes based on the location, pattern and time can serve as a valuable source of knowledge for them either from strategic or tactical...
Electroencephalogram (EEG) data is used for a variety of purposes, including brain-computer interfaces, disease diagnosis, and determining cognitive states. Yet EEG signals are susceptible to noise from many sources, such as muscle and eye movements, and motion of electrodes and cables. Traditional approaches to this problem involve supervised training to identify signal components corresponding to...
The power transformer is an important equipment in the power system. Its running state is directly related to the safe and stable operation of the power grid. The volume fraction of the dissolved gas in the oil of the transformer body and its variation law are closely related to the fault mode of the transformer, therefore, the dissolved gas analysis (DGA) technology in transformer oil is widely used...
Fibromyalgia (FM) is a widespread painful disease that has a 2–8% prevalence. Its diagnosis is generally performed by American College of Rheumatology (ACR) criteria. However, these criteria are subjective and their reliability is controversial. In this study, painful stimulation and Transcutaneous Electrical Nerve Stimulation (TENS) were applied to both hands of healthy controls and FM patients and...
The concept of internet finance has attracted increasing attention in recent years. As a result, more and more online peer-to-peer (P2P) lending platforms have been established at home and abroad. It is actually meaningful to predict investment amounts of online lenders in the following period. In this paper, we propose a Hybrid Investment Prediction Model (HIPM), an effective non-linear prediction...
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