Aiming at the time complexity of singular value spectrum weighted Hankel SVD filtering algorithm, a clutter suppression algorithm for ultrasonic color Doppler imaging based on BP neural network model is proposed in this paper. Firstly, using the PRF data collected by portable ultrasound instrument, we verify the singular value weighted Hankel SVD filtering algorithm, and the results show that the algorithm has high accuracy; Then, the BP neural network model is established based on the input and output data of singular value weighted Hankel-SVD filtering algorithm; Finally, the clutter suppression algorithm of ultrasonic color Doppler imaging based on BP neural network model is established. The experimental results show that compared with Hankel SVD filtering algorithm, the clutter suppression algorithm proposed in this paper greatly shortens the operation time without reducing the accuracy, so as to improve the real-time performance of the filtering algorithm.

Financial markets have become more and more complex, so has been the number of data sources. Stock price prediction has hence become a tough but important task. The time dependencies in stock price movements tend to escape from traditional models. In this work, a hybrid ARIMA-LSTM model is suggested to enhance accuracy of stock price forecasts. Based on time series indicators like adjusted closing prices of S&P 500 stocks over a decade (2010–2019), the ARIMA-LSTM model combines influences of both autoregressive time series forecasting with the substantial sequence learning property of LSTM. Data preprocessing in all aspects including missing values interpolation, outlier’s detection and data scaling – Min-Max guarantees data quality. The model is trained on 90/10 training/testing split and met with main performance metrics: MaE, MSE & RMSE. As indicated in the results, the proposed ARIMA-LSTM model gives a MAE value and MSE value of 0.248 and 0.101 respectively and RMSE of 0.319, a measure high accuracy on stock price prediction. Coupled comparative analysis with other Artificial Neural Networks (ANN) and BP Neural Networks (BPNN) are examples of machine learning reference models, further illustrates the suitability and superiority of ARIMA-LSTM approach as compared to the underlying models with the least MAE and strong predictive capability. This work demonstrates the efficiency of integrating the classical time series models with deep learning methods for financial forecasting.