What is data preprocessing for LSTM models?

What is data preprocessing for LSTM models?

Data preprocessing for LSTM models involves preparing your data to improve model performance, including normalization, handling missing values, and transforming sequences.

Why is normalization important in this context?

Normalization helps to scale the input data, ensuring that all features contribute equally to the model's learning process, which is crucial for LSTMs.

Can you explain how to handle missing values?

You can handle missing values by techniques like imputation, where you fill in missing data with mean, median, or mode, or by removing rows with missing values.

What kind of transformations are typically applied to the data?

Common transformations include one-hot encoding for categorical variables and sequence padding to ensure uniform input lengths for LSTM.