Research on Practical Intelligent Mode of Digital Image Economy Based on Improved Genetic Multilayer Neural Network

In the context of economic globalization and digitization, the current financial field is in an unprecedented complex situation. The methods and means to deal with this complexity are developing towards image intelligence. This paper takes financial prediction as the starting point, selects the artificial neural network in the intelligent algorithm and optimizes the algorithm, forecasts through the improved multilayer neural network, and compares it with the traditional neural network. Through comparison, it is found that the prediction success rate of the improved genetic multilayer neural network increases with the increase of the dimension of the input image data. This shows that, by adding more technical indicators as the input of the combined network, the prediction efficiency of the improved genetic multilayer neural network can be further improved and the advantage of computing speed can be maintained.

Fault Detection, Diagnosis, and Isolation Strategy in Li-Ion Battery Management Systems of HEVs Using 1-D Wavelet Signal Analysis

Nowadays, the wavelet transformation and the 1-D wavelet technique provide valuable tools for signal processing, design, and analysis, in a wide range of control systems industrial applications, audio image and video compression, signal denoising, interpolation, image zooming, texture analysis, time-scale features extraction, multimedia, electrocardiogram signals analysis, and financial prediction. Based on this awareness of the vast applicability of 1-D wavelet in signal processing applications as a feature extraction tool, this paper aims to take advantage of its ability to extract different patterns from signal data sets collected from healthy and faulty input-output signals. It is beneficial for developing various techniques, such as coding, signal processing (denoising, filtering, reconstruction), prediction, diagnosis, detection and isolation of defects. The proposed case study intends to extend the applicability of these techniques to detect the failures that occur in the battery management control system, such as sensor failures to measure the current, voltage and temperature inside an HEV rechargeable battery, as an alternative to Kalman filtering estimation techniques. The MATLAB simulation results conducted on a MATLAB R2020a software platform demonstrate the effectiveness of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.

Short-Term Forecasting in Bitcoin Time Series Using LSTM and GRU RNNs

In recent years, bitcoin has become a very attractive investment in financial industry, which is not controlled by governments, but is based on trust between transfers under the technology of block chain. Hence, forecasting future bitcoin cryptocurrency values is a problem that has attracted the attention of many researchers in the field, while proving to be a very challenging problem. This work presents an experimental analysis using LSTM and GRUs for forecasting bitcoin values in a minute-granulated time for the entire next day. To this end we also present our methodology for conducting the experiments. The final goal is to create the core of a financial prediction tool around the RNNs. In our experiments, we achieved interesting results such as a SMAPE of 0.0002, a RMSE of US$ 3.844 and a rRMSE of 0.0028 in a day where bitcoin rates vary from US$ 13.2K and US$ 14.6K, surpassing the results of SMAPE found in the literature and proposed limit of SMAPE smaller than 0.007 for forecasts.

Exploring propagation factors of social media moods for stock prices prediction

Weibo, the most widely-used social media in China, makes researchers highly regard its profound impact in public and gather moods for social computing and analysis, such as financial prediction. Most existing literatures concern excessively on text semantic or sentiment mining techniques, but neglect the procedure of moods dissemination and its factors. This paper proposes an integrated framework of social media moods mining, which creatively focuses on information transmission and propagating factors analysis, to predict stock prices more accurately. For the part of propagating factors on social media, several essential factors are distinguished in the dissemination process, such as emotional absorption of forwarding, influence of content and poster, user categories, release time, etc. to optimize the fitting effect of original model. And the count of forwarding also matters on predicting stock prices. Searching a given finance-related keyword, from Weibo we collected over 500,000 micro-blogs and their user information. Then we adopt the proposed integrated framework to predict stock price fluctuation, as well as the simple neural network method. Experiments demonstrate that the former outperformed the latter. The results also show that user categories and the count of forwarding differ on the lag phase of influence. And more, this paper studies the fitting effect of prediction models for different periods of the stock curve. The results indicate that the model works the best in the rising periods of stock prices curves, relatively well in the declining and the worst in the random fluctuating.

Development of Financial Prediction System using Natural Lang Processing

In financial stock market, final prices modify dayto-day at the end of each meeting. These modifications occur because of many issues that disturb the prices of the stocks. In our implementation, we used K-means algorithm to accurately calculate final prices by applying a data mining methodology. We examine and identify the most influential factors of Dubai Financial Stock Market prices. The main aim of this process is to help depositors to plan their future stock chances precisely. Two algorithms namely supervised and unsupervised algorithms are used in this method. It provides an efficient mechanism for the highly available CPU intensive process of big data analysis with help of cloud computing framework for analyzing and predicting the market closing values on the basis on external factors.