基于R语言neuralnet包的零售企业销售额预测模型优化咨询

Hey there! Let's break down why your neural net isn't beating linear regression for retail sales forecasting, and walk through concrete fixes tailored to your 2300 quarterly records and 18-feature dataset:

Your current code sets linear.output=F—that's a huge issue! The logistic activation function squashes outputs to the 0-1 range, which makes zero sense for predicting continuous sales values. For regression tasks like this, you must set linear.output=TRUE so the output layer uses a linear activation function, letting it predict the full range of sales figures. This alone could drastically improve your model's performance.

Linear regression is robust to unnormalized data, but neural nets thrive on scaled features. Here's what to do:

• Normalize/Standardize All Features and Target: Use scale() (for standardization) or min-max scaling to bring every input feature and your sales target into a consistent range (e.g., 0-1 or mean=0, std=1). Make sure to apply the same scaling parameters from your training set to the test set—don't re-scale using test data stats!
• Leverage Time Series Characteristics: Your data is quarterly, so retail sales almost certainly have seasonal trends. Add:
  • Lag features: Include sales values from the previous 1-4 quarters (e.g., sales_lag1, sales_lag4)—this lets the model capture recurring patterns.
  • Seasonal dummy variables: Create binary columns for Q1, Q2, Q3, Q4 to explicitly model quarterly spikes/dips.
• Prune Redundant Features: Use cor(train_$sales, train_[,-target_col]) to drop features with near-zero correlation to sales. You can also use LASSO regression to identify the most impactful features—reducing noise will help the neural net focus on what matters.

Your current hidden layer setup (c(14,12,4)) might be overcomplicating things for 2300 samples. Try these adjustments:

• Start Small: Begin with a single hidden layer (e.g., 5-10 neurons) instead of three. Deep networks are prone to overfitting when you don't have massive datasets. You can gradually add layers/neurons if simpler setups still underperform.
• Swap Activation Functions: Replace act.fct = "logistic" with act.fct = "rectified" (ReLU) for hidden layers. ReLU avoids the gradient saturation problem that plagues logistic functions, making training more efficient for regression tasks. Keep the output layer linear (via linear.output=TRUE).

Tweak these neuralnet arguments to get better convergence:

• Algorithm Choice: You're using rprop+—try switching to "backprop" with a small learning rate (e.g., learningrate=0.001). Backpropagation with momentum can be more stable for regression tasks. Adjust learningrate.factor if the model isn't converging or is overshooting.
• Lower the Threshold: threshold=0.01 might stop training too early. Try reducing it to 0.001 to let the model minimize error further, but monitor for overfitting.
• Use the Best Repetition: With rep=10, you're training 10 models. Extract the one with the lowest SSE using nn$result.matrix—don't just use the last one!

Neural nets love to memorize training data, so take these steps:

• Time-Series Cross-Validation: Don't use random K-fold CV—use rolling window validation. For example, train on the first 2000 quarters, test on 2001-2300, then roll the window forward (train on 1-2001, test on 2002-2300) to evaluate performance consistently.
• Early Stopping: Even though neuralnet doesn't have built-in early stopping, you can implement it manually: track validation error during training, and halt if it stops improving for several iterations.
• Simplify Further: If overfitting persists, reduce the number of hidden neurons or drop more features.

Make sure you're comparing apples to apples:

• Use the exact same train/test split for both the neural net and linear regression.
• For metrics, look beyond just SSE—check MAE (mean absolute error) and MAPE (mean absolute percentage error) to get a better sense of real-world forecasting accuracy for sales.

Once you fix the linear.output setting and work through these steps, you should see a noticeable jump in your neural net's performance.

内容的提问来源于stack exchange，提问作者hn11