How do you explain and interpret the results and predictions of neural network forecasting models?

1 Why explain and interpret neural network forecasting models?

Neural network forecasting models can produce accurate and reliable predictions, but they can also be prone to errors, biases, and overfitting. Explaining and interpreting neural network forecasting models can help you identify and correct these issues, as well as improve your trust and confidence in the models. Moreover, explaining and interpreting neural network forecasting models can help you communicate your findings and recommendations to different audiences, such as stakeholders, customers, or regulators, who may have different levels of technical expertise and expectations.

3 How to measure the performance of neural network forecasting models?

In order to explain and interpret neural network forecasting models, one of the initial steps is to measure their performance on the data. To evaluate how well the models fit the data, predict the future, and generalize to new situations, you can use various metrics and methods. These include Mean Absolute Error (MAE) and Mean Squared Error (MSE) for measuring average deviation of predictions from actual values, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) for measuring relative deviation of predictions from actual values, R-squared and Adjusted R-squared for gauging how much of the variation in the data is explained by the model, and Cross-Validation and Hold-Out Validation for assessing how the model functions on unseen data by splitting the data into training and testing sets.

5 How to analyze the features and weights of neural network forecasting models?

To further explain and interpret neural network forecasting models, you can analyze the features and weights of the models. Feature importance can measure how much each input variable contributes to the prediction, while partial dependence plots show how the prediction changes as a function of one or more input variables. Additionally, sensitivity analysis measures how the prediction changes as a result of small perturbations in the input variables, and weight visualization shows how the model assigns weights to the connections between the nodes in each layer. Examining these techniques and tools can help you understand how the input variables influence the output predictions, and how the model learns from the data.

6 How to communicate the results and predictions of neural network forecasting models?

When explaining and interpreting neural network forecasting models, it is important to communicate the results and predictions clearly, effectively, and persuasively. You can tailor your explanation to the audience’s background, goals, and expectations. Also, use simple and concise language by avoiding jargon and technical terms. Visual aids like graphs, tables, and diagrams can be used to support your explanation and interpretation. Additionally, provide context and evidence such as data sources, assumptions, limitations, and uncertainties to justify your explanation. Invite feedback and questions by encouraging dialogue, listening actively, and addressing concerns or objections.