RNN输入前的数据归一化疑问：多特征序列数据处理咨询

Hey there! Let's break down how to handle normalization for your RNN input (2 samples, sequence length 3, 3 features: 2 numeric, 1 binary) — this is a super common question when working with sequential models, so I'm glad you asked.

Core Rule: Don't Normalize Each Sample Independently #

First off, you almost never want to normalize each sample on its own unless you have a very specific use case where each sample is a completely isolated, unrelated time series and your model doesn't need to learn consistent feature scales across samples. Here's why:

• RNNs share weights across all samples and time steps during training. If you normalize each sample separately, the same normalized value (like 0.5) could represent wildly different raw values across samples. This confuses the model, as it can't learn stable patterns from the features.

Correct Normalization Approach #

1. Global Normalization for Numeric Features #

For your first two numeric features, use global normalization based on your entire training set statistics:

• Split your data into training, validation, and test sets first. Critical note: Always calculate normalization stats (mean, std, min, max) using only the training set, then apply those stats to validation and test sets to avoid data leakage.
• Choose a normalization method based on your feature distribution:
  • Z-score Standardization (best for normally distributed features):

    # Calculate using training set's all time steps for the first numeric feature
    mean = training_data[:, :, 0].mean()
    std = training_data[:, :, 0].std()
    normalized_feature1 = (data[:, :, 0] - mean) / std
    

  • Min-Max Scaling (good for skewed distributions or when you need values in [0,1]):

    min_val = training_data[:, :, 0].min()
    max_val = training_data[:, :, 0].max()
    normalized_feature1 = (data[:, :, 0] - min_val) / (max_val - min_val)
    

  • Repeat this process for your second numeric feature, using its own training set stats.

2. Leave Binary Feature As-Is #

Your third binary feature (0/1) doesn't need any normalization. Its scale is already consistent across all samples and time steps, so you can feed it directly into the RNN without modification.

Quick Caveat for Time Series Prediction #

If this is a time series forecasting task, make sure you don't use future data to compute normalization stats. For example, if you're predicting future values based on past sequences, calculate stats using only data up to each training time step — never include future data points in your mean/std/min/max calculations.

内容的提问来源于stack exchange，提问作者Ploo