While text data requires tokenization and embedding, time series data presents its own set of preprocessing needs before it can be effectively used by Recurrent Neural Networks. Time series often consist of measurements taken at successive points in time, potentially with multiple variables recorded at each step. Like other data types used in neural networks, numerical stability and consistent feature representation are important. Furthermore, we need to structure the continuous flow of time series data into discrete sequences suitable for RNN inputs.

This section covers two primary preprocessing steps for time series data: normalization and windowing. We'll also touch upon handling features and potential missing values.

Normalization and Scaling

Neural networks, including RNNs, generally train more effectively when input features are on a similar scale. Time series data can easily have features with vastly different ranges (e.g., temperature in Celsius vs. atmospheric pressure in Pascals). Large input values can lead to large gradients, potentially causing unstable training (exploding gradients), while very small values might slow down learning.

Two common scaling techniques are Min-Max Scaling and Standardization:

Min-Max Scaling

This method scales the data to a fixed range, usually [0, 1] or [-1, 1]. The formula for scaling to [0, 1] is:

X_{scaled} = \frac{X - X_{min}}{X_{max} - X_{min}}

Where $X_{min}$ and $X_{max}$ are the minimum and maximum values of the feature in the training dataset.

Pros: Guarantees data falls within a specific range, which can be beneficial for certain activation functions (like sigmoid, although less common inside modern RNN cells like LSTMs/GRUs which often use tanh).
Cons: Highly sensitive to outliers. A single extreme value in the training set can compress the rest of the data into a very small range. The exact min/max might not be known in advance for future data.

Standardization (Z-score Normalization)

This method scales data to have a mean of 0 and a standard deviation of 1. The formula is:

X_{scaled} = \frac{X - \mu}{\sigma}

Where $\mu$ is the mean and $\sigma$ is the standard deviation of the feature in the training dataset.

Pros: Less sensitive to outliers compared to Min-Max scaling. Centers the data around zero. It's a very common and often effective choice.
Cons: Doesn't guarantee a fixed range for the scaled data.

Important Consideration: Fitting the Scaler

A significant point in preprocessing time series (and most other machine learning data) is that you must fit the scaler (calculate $\mu$ , $\sigma$ , $X_{min}$ , $X_{max}$ ) only on the training data. You then use this fitted scaler to transform the training, validation, and test sets. Fitting the scaler on the entire dataset, including validation or test data, introduces information leakage from the future or unseen data into the training process, leading to overly optimistic performance estimates.

# Conceptual Python example using scikit-learn
from sklearn.preprocessing import StandardScaler
import numpy as np

# Assume train_data, validation_data, test_data are NumPy arrays
# Shape might be (num_samples, num_features) before windowing

scaler = StandardScaler()

# Fit ONLY on training data
scaler.fit(train_data)

# Apply the *same* fitted scaler to all datasets
train_scaled = scaler.transform(train_data)
validation_scaled = scaler.transform(validation_data)
test_scaled = scaler.transform(test_data)

# Later, when making predictions on new data, use the same scaler:
# new_data_scaled = scaler.transform(new_data)

# To revert predictions back to original scale (if needed):
# predictions_original_scale = scaler.inverse_transform(predictions_scaled)

Windowing: Creating Input/Output Sequences

RNNs process data sequentially. For a typical time series forecasting task, we don't feed the entire history as one sequence. Instead, we use a "sliding window" approach to generate multiple, smaller input sequences and their corresponding target values.

Imagine you have a univariate time series (one measurement per time step): [10, 20, 30, 40, 50, 60, 70, 80].

We need to decide:

Input Window Size (Lookback Period): How many past time steps the model should use as input features to make a prediction ( $N_{in}$ ).
Output Horizon: How many time steps into the future we want to predict ( $N_{out}$ ). For simplicity, let's start with predicting just the single next time step ( $N_{out}=1$ ).

Let's choose an input window size $N_{in} = 3$ and an output horizon $N_{out} = 1$ . We slide this window across the data:

Sample 1: Input [10, 20, 30] -> Target [40]
Sample 2: Input [20, 30, 40] -> Target [50]
Sample 3: Input [30, 40, 50] -> Target [60]
Sample 4: Input [40, 50, 60] -> Target [70]
Sample 5: Input [50, 60, 70] -> Target [80]

Each "Input -> Target" pair becomes one sample for training or evaluation. The input part will form the time_steps dimension of our RNN input tensor.

Types of Windowing Tasks

Many-to-One: This is the example above. Multiple past steps (N_{in}) are used to predict a single future step (N_{out}=1). Common for simple forecasting. The RNN typically processes the input sequence, and a Dense layer is applied to the final hidden state to produce the output prediction.
Many-to-Many (Single Output Sequence): Multiple past steps (N_{in}) predict multiple future steps (N_{out} > 1). For example, use 3 past steps to predict the next 2 steps.
- Sample: Input [10, 20, 30] -> Target [40, 50]
- This often requires the RNN to output a sequence (e.g., setting return_sequences=True in Keras/TensorFlow for intermediate layers, or using specific decoder structures).
Many-to-Many (Aligned Input/Output): Each input time step maps to an output time step. This is less common in pure forecasting but appears in tasks like sequence labeling.

The choice depends entirely on the problem you are trying to solve. For forecasting, Many-to-One (predicting the next step) and Many-to-Many (predicting multiple future steps) are frequent patterns.

Implementing Windowing

This is often done using basic array manipulation, for instance, with NumPy slicing or dedicated library functions (like tf.keras.utils.timeseries_dataset_from_array in TensorFlow).

# Conceptual Python function for windowing (Many-to-One)
def create_windows(data, input_width, label_width, shift):
    """
    Creates windowed data for time series forecasting.
    Args:
        data: The time series data (NumPy array, typically 2D: time x features).
        input_width: The number of time steps in the input window.
        label_width: The number of time steps in the output label window (usually 1 for simple next-step prediction).
        shift: The offset between the end of the input window and the start of the label window.
               For predicting the immediate next step(s), shift=label_width.
    Returns:
        inputs: A list or array of input windows.
        labels: A list or array of corresponding labels.
    """
    inputs = []
    labels = []
    total_window_size = input_width + shift
    end_index = len(data) - total_window_size + 1

    for i in range(end_index):
        input_slice = data[i : i + input_width]
        label_start_index = i + input_width + shift - label_width
        label_slice = data[label_start_index : label_start_index + label_width]
        inputs.append(input_slice)
        labels.append(label_slice)

    return np.array(inputs), np.array(labels)

# Example Usage:
# Assuming 'scaled_data' is our preprocessed time series (e.g., shape [1000, 1] or [1000, num_features])
INPUT_WIDTH = 24  # Use 24 hours of past data
LABEL_WIDTH = 1   # Predict 1 hour ahead
SHIFT = 1         # Predict the step immediately following the input window

inputs, labels = create_windows(scaled_data, INPUT_WIDTH, LABEL_WIDTH, SHIFT)

# Resulting shapes (approximate, depends on total data length):
# inputs.shape -> (num_samples, INPUT_WIDTH, num_features) e.g., (976, 24, 1)
# labels.shape -> (num_samples, LABEL_WIDTH, num_features) e.g., (976, 1, 1)
# If label_width is 1, often labels are squeezed: (num_samples, num_features)

This inputs array now has the shape (num_samples, time_steps, num_features), which is exactly what RNN layers expect (after grouping into batches).

Handling Timestamps and Features

Timestamps: If your time series is perfectly regular (e.g., measurements every hour on the dot), the timestamp itself might not be needed as direct input, as the sequence order implies the time progression. However, if time information is considered useful (e.g., seasonality, day of the week, time of day effects), you can engineer features from the timestamps (e.g., sine/cosine transformations of the hour, binary flags for weekends) and include them as additional input features alongside the measurements.
Multivariate Data: If you have multiple measurements at each time step (e.g., temperature, humidity, pressure), these naturally form the features dimension in your input tensor. Ensure all features are appropriately scaled (usually using the same method, like Standardization, fitting independently per feature on the training data). The windowing process remains the same, but each time step in the window will contain multiple feature values.

Dealing with Missing Data

Real-world time series often contain missing values. Common strategies include:

Forward Fill (ffill): Replace missing values with the last observed value. Good if samples are frequent and values don't change abruptly.
Backward Fill (bfill): Replace missing values with the next observed value.
Interpolation: Estimate missing values based on surrounding points (e.g., linear interpolation).
Mean/Median Imputation: Replace missing values with the mean or median of the feature (calculated from the training set). Often less effective for time series as it ignores temporal dynamics.

It's generally best to handle missing values before scaling and windowing, although the specific choice depends on the nature of the data and the extent of missingness. If using imputation, remember that this also introduces assumptions into your data. Masking (covered earlier for text padding) can sometimes be adapted for missing steps, but standard RNN layers might not directly support it without custom implementations or specific framework features.

By applying normalization and windowing, you transform raw time series measurements into structured, scaled sequences ready for training powerful recurrent models like LSTMs and GRUs. Remember to apply these steps consistently across your training, validation, and test datasets, always deriving scaling parameters and imputation statistics solely from the training portion.