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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...
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...
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...
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...
Predicting stock prices is an important task of financial time series forecasting, which is of great interest to stock investors, stock traders and applied researchers. Many machine learning techniques have been used in recent times to predict the stock price, including regression algorithms which can be useful tools to provide good accuracy of financial time series forecasting. In this paper, we...
Stock Price movement is a non-linear Financial Time Series. Short-term stock price prediction is best done through Technical Analysis using Artificial Intelligence to detect patterns and trigger buy and sell signals. This paper aims to develop an expert system using Technical Analysis and Support Vector Machines that emulates the reasoning and decision-making process of a technical analyst as applied...
Accurate forecasting of directional changes in stock prices is important for algorithmic trading and investment management. Technical analysis has been successfully used in financial forecasting and recently researchers have explored the optimization of parameters for technical indicators. This study investigates the relationship between the window size used for calculating technical indicators and...
Rawal Dam is a strategic asset for the twin cities of Islamabad and Rawalpindi in Pakistan being the main source of drinking as well as agricultural water supplies. The low-lying areas of the reservoir are being affected by reservoir storage and spillways' discharge. For the effective management, modeling techniques would not only be cost effective but also help the water managers in predicting the...
The residual life prediction of aero-engine is important for ensuring flight safety and reducing operating costs for airlines. Since there are varied performance parameters of aero-engine, it is difficult to use comprehensively these performance parameters to predict the residual life. This paper exploits Support Vector Regression Machine (SVR) in predicting the trend of varied performance parameters...
In the area of prognostics and health management, data-driven methods increasingly show the superiority against model-based method due to the complex relationships and learn trends available in the data captured without the need for specific failure models. This paper uses Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM) to build a model for non-stationary time series prediction...
Multiple Kernel Learning (MKL) is one of recent approaches to choose suitable kernels from a given pool of kernels by exploring the combinations of multiple kernels. For linear kernel, the target kernel is a linear combination some base kernels. However, some literatures suggest that a linear combination of kernels cannot consistently outperform either the uniform combination of base kernels or simply...
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