Table
of Contents Background
Summary
Basic definition of LSTM
LSTM implementation steps
Soybean futures price prediction based on long-term and short-term neural network LSTM
MATALB code
Effect diagram
Results analysis
Outlook
Reference papers
back view
Futures forecasting is a mathematical problem that is difficult to predict. There are many models for research, but they all have limitations and the accuracy rate is not high. In this paper, LSTM is used for soybean futures price forecasting, and deep learning programming is realized. No fitting formula is required, and approximation The effect is good.
Abstract
LSTM principle, MATALB programming long-term and short-term neural network LSTM soybean futures price prediction,
Basic definition of LSTM
LSTM is a type of neural network that contains LSTM blocks (blocks) or others. In literature or other materials, LSTM blocks may be described as intelligent network units because they can memorize values of indefinite length of time. There is a The gate can determine whether the input is important enough to be remembered and whether it can be output.
There are four S function units at the bottom of Figure 1. The leftmost function may become the input of the block according to the situation. The three on the right will pass through the gate to determine whether the input can be passed to the block. The second on the left is the input gate. If the output here is similar If it is at zero, the value here will be blocked and will not enter the next layer. The third one from the left is the forget gate, when this produces a value close to zero, the value remembered in the block will be forgotten. The fourth and rightmost input is the output gate, which can determine whether the input in the block memory can be output.
Figure 1 LSTM model
Figure 1 LSTM model
There are many versions of LSTM, one of the important versions is GRU (Gated Recurrent Unit), according to Google's test, the most important in LSTM is Forget gat