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List of contents of this article:

• 1. How to use machine learning to solve the prediction problems of complex systems
• 2,Hearthstone arena How does the gray plant disease system predict the model?
• 3, What steps has the predictive maintenance system gone through before the predictive analysis?

How to use machine learning to solve the prediction problems of complex systems

Prediction based on technical indicators: Technical indicators are quantitative indicators that reflect the market situation, such as moving averages , MACD, etc. These indicators can be analyzed through machine learning algorithms to predict the trend of stock prices.Fundamental-based forecast: Fundamental refers to the financial situation of the company to which the stock belongs, the development of the industry and other information.

Integration method: integrating multiple different prediction models or algorithms can improve the accuracy of prediction. For example, use random forest or Boosting methods to integrate multiple decision tree models. Automated decision-making: Combining machine learning and artificial intelligence with automated decision-making systems can improve efficiency while ensuring accuracy.

Use the neural network model for prediction: After completing the training and testing, we can use the neural network model for prediction. The forecast results can help us understand the future trend. Use neural network prediction to accurately predict future trends. Neural network prediction can help us predict various future trends.

How does the gray plant disease system predict the model?

To make gray prediction, we must first identify the degree of difference in the development trend between the system factors, that is, carry out correlation analysis, and then generate and process the original data to find the law of system changes, generate data sequences with strong regularity, and then establish a corresponding differential equation model to predict whether things The situation of the development trend.

There are many gray prediction models, and the GM (1,1) model is the most widely used. The first number represents the first-order differentiation, and the second number 1 represents only one data sequence.

The gray system analysis method is to identify the similarity or difference of the development trend between the system factors, that is, to conduct correlation analysis, and to seek the law of system change by generating and processing the original data.

Its main contents include a theoretical system based on gray hazy sets, an analysis system based on gray association space, a method system based on gray sequence generation, a model system based on gray model (GreyModel) as the core, and systematic analysis, evaluation, modeling and prediction , a technical system with decision-making, control and optimization as the main body.

Because excel is enough to do these additions and subtractions.I once successfully solved the modeling questions in 2005 with excel, with gray GM (1, 1). However, if you want to use matlab, it's okay, just use the for loop.

The gray prediction model is also known as the GM (GrayModel) model. The GM model is an approximate differential differential equation model, which has differential, differential, exponential compatibility and other properties. The model parameters are adjustable, and the structure changes over time, breaking through the general modeling requirements with a lot of data, and it is difficult to obtain "micro Limitations of the nature of division [1].

What steps has the predictive maintenance system gone through before the predictive analysis?

1. This is toSeek to develop a predictive maintenance platform or a complete ecosystem whose architecture should be modular so that sensing, status monitoring and evaluation, diagnosis, prediction and other functions can be easily added or strengthened.

2. The structural analysis of DFMEA is to identify and decompose the design into systems, subsystems, components and parts for technical risk analysis. Structural analysis of PFMEA is to determine the manufacturing system and decompose it into process items, process steps and process work elements.

3. Qualitative prediction. Qualitative prediction is a subjective judgment, which is based on estimation and evaluation. Common qualitative forecasting methods include: general forecasting, market research method, group discussion method, historical analogy, Delphi method, etc.