Learn More
A new algorithm is presented for solving Troesch’s problem. The numerical scheme based on the modified homotopy perturbation technique is deduced. Some numerical experiments are made. Compared with the variational iteration method and the Adomian decomposition method, the scheme is shown to be highly accurate, and only a few terms are required to obtain(More)
Filtering has been an enabling technology and has found ever-increasing applications. There are two main classes of digital filters: finite impulse response (FIR) filters and infinite impulse response (IIR) filters. FIR filter can be guaranteed to have linear phase and are always stable filters, so FIR filters is widely applicable. The differential(More)
Traditional time series classification problem with supervised learning algorithm needs a large set of labeled training data. In reality, the number of labeled data is often smaller and there is huge number of unlabeled data. However, manually labeling these unlabeled examples is time-consuming and expensive, and sometimes it is even impossible. Although(More)
Multivariate time series (MTS) classification is an important topic in time series data mining, and lots of efficient models and techniques have been introduced to cope with it. However, early classification on imbalanced MTS data largely remains an open problem. To deal with this issue, we adopt a multiple under-sampling and dynamical subspace generation(More)
Determining appropriate loan-to-value ratios of commodity collateral can make banks mitigate credit risk of inventory financing effectively. Based on reduced-form approaches, this paper establishes a basic model on the determination of loan-to-value ratios. In this model, some factors, such as exogenous default probability, price volatility of commodity(More)