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Content introduction
?Time series prediction is an important problem in the fields of machine learning and data science. It involves predicting future trends and patterns based on past data. Long short-term memory neural network (LSTM) is a special type of recurrent neural network that is widely used in time series prediction tasks. This article will introduce the algorithm steps for realizing data time series prediction based on the attention mechanism (attention-LSTM) of LSTM.
First, we need to understand the basic concepts of LSTM and attention mechanism. LSTM is a recurrent neural network with long-term memory capabilities. It solves the problems of vanishing and exploding gradients by controlling the flow of information, thereby better capturing long-term dependencies in time series data. Attention is a mechanism that allows a network to selectively focus on specific time steps when processing an input sequence. This mechanism can help the network better understand and utilize key information in the input sequence.
Next, we will introduce the algorithm steps of using LSTM with attention mechanism to achieve data time series prediction.
Step 1: Prepare the data First, we need to prepare the data sets for training and testing. The data set should contain historical time series data and corresponding target values. We can use the pandas library in Python to load and process data.
Step 2: Data preprocessing Before training the LSTM model, we need to preprocess the data. This involves normalizing the data to a smaller range and converting it into an input format suitable for the LSTM model. We can use the MinMaxScaler class in the scikit-learn library for normalization and the numpy library to convert the data format.
Step 3: Build an LSTM model Next, we need to build an LSTM model. We can use the Keras library to implement the LSTM model. When building a model, we can choose to add an attention layer to help the model better understand the key information in the input sequence.
Step 4: Model training After building the model, we need to train it using training data. We can use the fit function in the Keras library to train the model. During the training process, we can choose to use appropriate optimizers and loss functions, and choose appropriate training parameters.
Step 5: Model evaluation After completing the model training, we need to evaluate the performance of the model. We can use the test data set to evaluate the predictive power of the model. We can calculate the error between the predicted value and the true value and evaluate the performance of the model using various metrics such as mean square error and mean absolute error.
Step 6: Model prediction Finally, we can use the trained model to perform time series prediction. We can feed new input sequences into the model and use the model to predict future trends and patterns.
Summary: This article introduces the algorithm steps for realizing data time series prediction based on the attention mechanism of LSTM. By using LSTM and attention mechanisms, we can better capture long-term dependencies in temporal data and better understand and utilize key information in input sequences. This method performs well in many time series forecasting tasks and is widely used in financial forecasting, weather forecasting and other fields. I hope this article will help you understand and apply the role of LSTM and attention mechanisms in time series prediction.
Part of the code
%% Clear environment variables</code><code>warning off % Close alarm information</code><code>close all % Close open figure window</code><code>clear % Clear variables</code><code>clc % clear command line</code><code>?</code><code>%% import data</code><code>res = xlsread('dataset.xlsx');</code><code>?</code><code>%% divide the training set and test set</code><code>temp = randperm(357);</code><code>?</code><code>P_train = res(temp(1: 240), 1: 12)';</code><code>T_train = res(temp(1: 240), 13)';</code><code>M = size(P_train , 2);</code><code>?</code><code>P_test = res(temp(241: end), 1: 12)';</code><code>T_test = res(temp(241 : end), 13)';</code><code>N = size(P_test, 2);</code><code>?</code><code>%% data normalization</code><code>[p_train, ps_input] = mapminmax(P_train, 0, 1);</code><code>p_test = mapminmax('apply', P_test, ps_input);</code><code>t_train = ind2vec(T_train) ;</code><code>t_test = ind2vec(T_test );
Operation results
References
[1] Xiao Ting. Research on multi-variable time series short-term forecast model based on neural network[J].[2023-10-25].
[2] Zhang Yu, Chen Guangshu, Li Jitao, et al. Research and application of CNN-LSTM time series prediction method based on attention mechanism [J]. Journal of Inner Mongolia University: Natural Science Edition, 2022.