2024年3月8日发(作者：苹果官方旗舰店价目表)

n-beats用法

N-BEATS, which stands for Neural basis expansion analysis for

interpretable time series forecasting, is a deep learning model developed

by Oreshkin et al. in 2019. It is designed to provide interpretable and

accurate predictions for time series data. In this article, we will take a

step-by-step approach to understand and apply the N-BEATS

framework.

1. Introduction to N-BEATS:

N-BEATS is a fully automatic and interpretable time series forecasting

model that aims to capture non-linear patterns present in the data. It

consists of a stack of fully connected layers, called the backcast and

forecast sub-networks, which transform the input sequence into a

lower-dimensional representation and then produce predictions,

respectively.

2. Understanding the N-BEATS Architecture:

The N-BEATS model architecture comprises three main components: the

backcast sub-network, the forecast sub-network, and the loss function.

2.1 Backcast Sub-Network:

The backcast sub-network takes a window of historical data as input and

computes a sequence of lower-dimensional representations. It is

responsible for capturing the underlying patterns and dependencies

present in the observed time series.

2.2 Forecast Sub-Network:

The forecast sub-network takes the outputs from the backcast

sub-network and produces predictions for future time steps. It aims to

learn the relationships between the lower-dimensional representations

and the future time series values.

2.3 Loss Function:

The N-BEATS model uses a combination of quantile regression and mean

squared error loss functions to optimize the forecasts. The quantile

regression loss helps to capture the uncertainty in the predictions by

optimizing for a range of percentiles, while the mean squared error loss

penalizes the model for large errors.

3. Implementing N-BEATS:

To implement N-BEATS, you can follow these steps:

3.1 Data Preparation:

Prepare your time series data by splitting it into training, validation, and

testing sets. Ensure that your data is properly formatted and

standardized for training the model.

3.2 Model Configuration:

Decide on the hyperparameters of the N-BEATS model, such as the

number of layers, the dimensionality of the representations, and the size

of the input and output windows.

3.3 Training the Model:

Train the N-BEATS model using the training set and monitor its

performance on the validation set. Use an appropriate optimization

algorithm and loss function to update the model parameters iteratively.

3.4 Evaluating the Model:

Evaluate the performance of the trained N-BEATS model on the testing

set. Calculate appropriate evaluation metrics such as mean absolute error,

mean squared error, and quantile loss to assess its accuracy and

interpretability.

4. Potential Applications of N-BEATS:

N-BEATS can be applied to various time series forecasting tasks, such as

predicting stock prices, electricity load forecasting, demand forecasting,

and weather prediction. Its interpretability makes it useful in domains

where understanding the underlying patterns and dependencies is

crucial.

5. Conclusion:

N-BEATS is a powerful deep learning model that combines fully

connected layers, quantile regression, and mean squared error loss

functions to provide accurate and interpretable time series forecasts. By

following the steps outlined above, you can successfully implement and

apply the N-BEATS framework to your own time series forecasting tasks.