I have a set of parameters that have time series data. I have rearranged the input in a way that looks like
A.1 B.1 C.1 A.2 B.2 C.2 A.3 B.3 C.3
3. 2. 1. 2. 1. 1. 4. 4. 4
2. 1. 4. 4. 4. 2. 2. 4. 4
where A, B, and C are my parameters, each with three values of time series. The rows represents different instances. I am training a random forest with this input data. Then, I am plotting the importance of the parameters. Initially, my plots showed a bar for each one of the parameters with its time serie, for example A.1, A.2, A.3, … But I wanted to plot the A, B, and C. What I did was to reshape and add their importance, for example A=A.1+A.2+A.3 and so forth for each parameter,I am new to machine learning, so I want to ask if this procedure is appropiate or if I need to change anything in my process, thank you.

this is my code:

    from sklearn.model_selection import train_test_split
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.metrics import accuracy_score
    import numpy as np
    import matplotlib.pyplot as plt


    # Input data for 3 flights, each with 5 time steps and 2 parameters (Altitude and Velocity)
    data = [
    np.array([
    [3.06, 110, 310],
    [2.94, 128, 300],
    [2.88, 144, 299],
    [2.56, 128, 301],
    [2.38, 129, 301],
    [2.13, 142, 301],
    [2.25, 127, 303],
    [2.13, 162, 300],
    [1.75, 138, 298],
    [1.69, 142, 301]
    ]),
    np.array([
        [8, 135, 179],
        [7, 161, 180],
        [7, 117, 179],
        [6, 122, 180],
        [6, 132, 178],
        [5, 131, 180],
        [5, 136, 180],
        [4, 134, 179],
        [4, 136, 180],
        [3, 113, 179]
        ]),
    np.array([
        [1.13, 148, 91],
        [1.06, 113, 87],
        [0.75, 164, 92],
        [0.63, 141, 95],
        [0.56, 139, 85],
        [0.44, 142, 94],
        [0.25, 140, 90],
        [0.19, 144, 90],
        [0.13, 140, 92],
        [0   , 142, 93]
        ]),
    np.array([
        [1.13, 119, 299],
        [1,    121, 299],
        [0.88, 116, 299],
        [0.69, 117, 299],
        [0.63, 118, 301],
        [0.44, 118, 300],
        [0.38, 117, 299],
        [0.19, 119, 299],
        [0.13, 118, 300],
        [0.06, 120, 300]
        ])
    ]

    print(data)

    # Reshape the 3D input into a 2D format
   num_flights = len(data)
   num_timesteps, num_params = data[0].shape
   reshaped_data = np.concatenate(data).reshape(num_flights, -1)
   print(reshaped_data)

    # Create labels for the flights 
    labels = np.array([1, 1, 0, 0])

    # Split the data into training and test sets
    X_train, X_test, y_train, y_test = train_test_split(reshaped_data, labels, test_size=0.2,        random_state=42)

    # Train a Random Forest classifier
    clf = RandomForestClassifier(n_estimators=100, random_state=42)
    clf.fit(X_train, y_train)

    # Predict on the test set
    y_pred = clf.predict(X_test)

    # Calculate accuracy
    accuracy = accuracy_score(y_test, y_pred)
    print("Accuracy:", accuracy)

    # Get feature importances from the trained Random Forest model
    feature_importances = clf.feature_importances_
    print(feature_importances)

    # Reshape feature importances to match the original data shape
    reshaped_importances = feature_importances.reshape(num_timesteps, num_params)
    print(reshaped_importances)
    # Plot the feature importances for each parameter
    plt.figure(figsize=(10, 6))
    print(reshaped_importances.sum(axis=0))
     plt.bar(range(num_params), reshaped_importances.sum(axis=0), tick_label=['Altitude', 'GroundSpeed','FlightCourse'])
    plt.xlabel('Parameter')
    plt.ylabel('Importance')
    plt.title('Feature Importance of Parameters')
    plt.show()

I wanted to plot the importance of the features. I was getting the importance of parameters for its different time series, then I added the importances by parameter