To solve the problem where by the available on-site input data are too scarce to predict the level of groundwater, this paper proposes an algorithm to make this prediction called the canonical correlation forest algorithm with a combination of random features. To assess the effectiveness of the proposed algorithm, groundwater levels and meteorological data for the Daguhe River groundwater source field, in Qingdao, China, were used. First, the results of a comparison among three regressors showed that the proposed algorithm is superior in terms of forecasting variations in groundwater level. Second, the results of experiments were used to show the comparative superiority of the proposed method in terms of training time and complexity of parameter optimization. Third, using the proposed algorithm, the highest prediction accuracy was achieved by employing precipitation P(t − 2), temperature T(t), and groundwater level H(t) as the best time lag. This improved random forest regression model yielded higher accuracy in forecasting the variation in groundwater level. The proposed algorithm can also be applied to cases involving low-dimensional data.
Acquiring channel state information and mitigating multi-path interference are challenging for underwater acoustic communications under time-varying channels. We address the issues using a superimposed training (ST) scheme with a least squares (LS) based channel estimation algorithm. The training sequences with a small power are linearly superimposed with the symbol sequences, and the training signals are transmitted over all time, resulting in enhanced tracking capability to deal with timevarying underwater acoustic channels at the cost of only a small power loss. To realize the full potentials of the ST scheme, we develop a LS based channel estimation algorithm with superimposed training, where the Toeplitz matrix is used, which is formed by the training sequences, enabling channel estimation with superimposed training. In particular, a low-complexity channel equalization algorithm based on generalized approximate messaging passing (GAMP) is proposed, where the a priori, a posteriori, extrinsic means and variances of interleaved coded bits are computed, and then convert them into extrinsic log likelihood ratios for BCJR decoding. Its computational complexity is only in a logarithmic order per symbol. Moreover, the channel estimation, GAMP equalization and decoding are performed jointly in an iterative manner, so that the estimated symbol sequences can also be used as virtual training sequences to improve the channel estimation and tracking performance, thereby remarkably enhance the overall system performance. Moving communication experiments in Jiaozhou Bay (communication frequency 12 kHz, bandwidth 6 kHz, sampling frequency 96 kHz, symbol transmission rate 4 ksym/s) were carried out, and the experimental results verify the effectiveness of the proposed technique.INDEX TERMS Time-varying underwater acoustic channels, superimposed training, generalized approximate messaging passing, iterative turbo receiver
Predicting stock prices through historical data is a hot research topic. Stock price data is considered to be a typical time series. Recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent units (GRU) have been commonly employed to handle this type of data. However, most studies focus on the analysis of individual stocks, thus ignoring the correlation between similar stocks in the entire stock market. This paper proposes a clustering method for mining similar stocks, which combines morphological similarity distance (MSD) and kmeans clustering. Subsequently, Hierarchical Temporal Memory (HTM), an online learning model, is used to learn patterns from similar stocks and make predictions at last, denoted as C-HTM. The experiments on the price prediction show that 1) compared with HTM which has not learned similar stock patterns, C-HTM has better prediction accuracy, 2) in terms of short-term prediction, the performance of C-HTM is better than all baseline models.
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