Machine learning method for event occurrence prediction

Abstract

The invention provides a machine learning method for event occurrence prediction. The method aims at solving the problems of data sparsity and complexity in event occurrence prediction, key features can be mined from historical data, and the occurrence probability of future events can be predicted. The method comprises the steps of data source input, data preprocessing, feature extraction, attention mechanism, model training, prediction and output and the like. By introducing an attention mechanism, the model can pay attention to relevance and time sequence between events, and the accuracy and generalization ability of prediction are improved. In the model training process, historical event data with labels are used, a model is trained in a supervised learning mode, optimization algorithms such as cross entropy loss function minimization are applied, and model parameters are optimized. The invention provides a novel event occurrence prediction method which can provide effective support and help for research and application in related fields.

Description

Technical fields:

[0001] This invention relates to the field of machine learning, and in particular to a method for predicting whether an event will occur. Background technology:

[0002] In the field of machine learning, event prediction is an important research area. Traditional machine learning methods often require extensive feature engineering and domain knowledge to achieve good predictive results. In recent years, deep learning-based models have achieved remarkable results in fields such as image recognition and natural language processing. However, in the field of event prediction, these methods still have certain limitations due to the sparsity and complexity of data. Therefore, how to solve the problems of data sparsity and complexity in event prediction is a challenging issue. Summary of the Invention:

[0003] This invention provides a novel large-scale prediction model algorithm that can effectively handle complex data. Specifically, the large-scale prediction model algorithm of this invention includes the following steps.

[0004] Data source input: Collect data on whether historical events have occurred, including information such as the time, location, type, and relevant people involved in the event.

[0005] Data preprocessing: Cleaning and preprocessing the input data, including data deduplication, filling in missing values, and data normalization.

[0006] Feature extraction: Key features are extracted from historical event data using feature engineering techniques such as TF-IDF and word2vec. Deep learning techniques such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are then applied to further abstract and fuse these features.

[0007] Attention Mechanism: By calculating the temporal relevance and importance score of each feature, an attention mechanism is introduced to select the features most influential on event prediction. The application of a self-attention mechanism enables the model to focus on the correlation and temporal sequence between events.

[0008] Model training: The model is trained using labeled historical event data and supervised learning. Optimization algorithms, such as minimizing the cross-entropy loss function, are applied to optimize the model parameters.

[0009] Prediction and Output: Use the trained model to predict newly occurring events and present the prediction results to users in the form of visualizations or reports.

[0010] Compared with existing technologies, the prediction large model algorithm of the present invention has the following advantages:

[0011] Beyond the concept of time: The large-scale predictive model algorithm itself abandons the concept of time, but it is still within time when used. Users can first train the model using data from past events, and after the model is trained, it can completely predict and verify all events that have occurred in the past, present, and future.

[0012] Simplified feature engineering: This model can automatically mine key features from historical data, reducing the difficulty and cost of manual feature engineering.

[0013] Improve prediction accuracy: By introducing an attention mechanism, the model can more accurately predict the probability of an event occurring.

[0014] High data adaptability: This model can handle event data from different fields and has a certain ability to handle data sparsity.

[0015] Compared with the prior art, the innovations of this invention include the following points.

[0016] Introducing an attention mechanism: This method, by introducing an attention mechanism, can extract key features from historical data and predict the probability of future events. This is a relatively advanced technique in traditional machine learning methods.

[0017] Deep learning techniques: This method uses deep learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to further abstract and fuse features. This improves the model's expressive power and allows it to better handle complex event data.

[0018] Data sparsity handling capability: The model of this method has a certain data sparsity handling capability, which can adapt to event data in different domains. This is relatively rare in traditional machine learning methods.

[0019] In the field of event prediction: This method proposes a machine learning approach for event prediction, capable of forecasting the probability of future events. This has significant practical implications for preventing and responding to various events, such as natural disasters and financial risks. Attached image description:

[0020] Figure 1 This diagram illustrates the overall system architecture of a large-scale prediction model. It mainly includes the following components: data source input, data preprocessing, feature extraction, attention mechanism, model training, prediction, and output.

[0021] Figure 2 This diagram is a data processing flowchart, detailing the entire process from data input to prediction. It mainly includes data collection, data cleaning, data integration, feature extraction, data normalization, and deep data mining.

[0022] Figure 3 This diagram illustrates the training and validation process of a machine learning model. It mainly includes training set construction, cross-validation, hyperparameter tuning, and model validation.

[0023] Figure 4 This chart is an analysis of the prediction results, used to analyze and display the prediction outcomes. It mainly includes the prediction output, confidence intervals, result comparisons, and performance metrics.

[0024] Figure 5 This diagram illustrates the principle of calculating a weighted average of input information based on attention distribution. It mainly explains how the attention mechanism processes key-value pair inputs. Detailed implementation method:

[0025] Suppose we want to predict product sales on an e-commerce platform to help merchants develop inventory management and marketing strategies. We can implement this prediction on the dataset using the following steps:

[0026] Data source input: We need to collect historical sales data of product Q into an Excel spreadsheet, including product name, product number, listing time, product weight, sales volume, price, promotional activities, user ratings, product category, brand, and other information.

[0027] Data Preprocessing: We perform data cleaning and preprocessing on the input data, such as data deduplication: Using VBA macro code, the program automatically searches for and compares each corresponding item in the training data Q1, Q2...Qn pairwise. Let's take Q1 and Q2 as an example. If the values ​​of each item in Q1 and Q2 are found to be equal, the system will determine that Q1=Q2 and automatically delete Q2, achieving the effect of data deduplication. Missing Value Imputation: Using VBA macro code, missing values ​​are automatically searched. If the data in Q1 or Q2 is found to be incomplete or empty, the missing values ​​are filled with default values. Data Normalization: Redundant data is removed, such as removing information like product weight and shelf time, to normalize the training data. Finally, using the JSON key-value pair format of [variable name: variable value], these preprocessed data are packaged into a two-dimensional array matrix Q= [ A : c V : a E : d P : b ] V, P, A, and E represent four measurement dimensions: sales volume, price, promotional activities, and user ratings, respectively, while a, b, c, and d represent the values ​​corresponding to these four dimensions, which are used as the input layer matrix for training the neural network.

[0028] Feature extraction: Key features are extracted from historical product sales data using feature engineering techniques such as TF-IDF and word2vec. For example, we extract features such as product category, brand, price range, and user ratings, which are then used as the hidden layer matrix M of the neural network. [ P : 1 C : 1 E : 1 B : 1 ] The weight matrix is ​​calculated by multiplying the input layer and the hidden layer, and then... Weighted summation, plus bias operations and the sigmoid activation function: By removing linearization from feature nodes and combining it with deep learning techniques such as convolutional neural networks (CNN) and recurrent neural networks (RNN), the features are further abstracted and fused.

[0029] Attention Mechanism: By calculating the temporal relevance and importance score of each feature, an attention mechanism is introduced to filter out the features most influential on sales prediction. For example, we can calculate the temporal relevance and importance scores of features such as average sales volume, price, and number of promotions for each product, and select the most relevant features based on the score. A weighted average of the input information is calculated based on the attention distribution, and a selection mechanism is used to summarize the input information. .

[0030] Model Training: The model is trained using labeled historical product sales data and supervised learning. A cross-entropy loss function minimization optimization algorithm is applied. The model parameters are optimized to improve the accuracy and generalization ability of predictions.

[0031] Prediction and Output: The trained model is used to predict newly generated sales data, and the prediction results are output in the form of a report.

Claims

1. A machine learning method for predicting the occurrence of events, the method comprising the following steps: a. Collect historical event data; b. Clean and preprocess the data; c. Extract key features; d. Introduce an attention mechanism to predict the probability of future events.

2. The method of claim 1, wherein step c includes using deep learning techniques such as convolutional neural networks (CNN) and recurrent neural networks (RNN) to further abstract and fuse the features.

3. The method of claim 1 or 3, wherein the attention mechanism is implemented by calculating the temporal relevance and importance score of each feature.

4. The method as described in claim 1, wherein the model input is historical event data, which, after processing such as feature extraction and attention mechanism, outputs a prediction of the probability of future events.

5. A machine learning model for predicting event occurrence, the model comprising a preprocessing module, a feature extraction module, and an attention mechanism module.

6. The model as described in claim 7, wherein the preprocessing module includes steps such as data cleaning, deduplication, filling in missing values, and data normalization.

7. The model as described in claim 7 or 8, wherein the features The extraction module uses techniques such as TF-IDF and word2vec to extract key features from historical event data.

8. The model as described in claim 7 or 8, wherein the attention mechanism module selects the features most influential on event prediction by calculating the temporal relevance and importance score of each feature.

Images