multivariate xgboost time series

Question

I implemented a univariate xgboost time series using the following code,

def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):
    n_vars = 1 if type(data) is list else data.shape[1]
    df = pd.DataFrame(data)
    cols = list()
    # input sequence (t-n, ... t-1)
    for i in range(n_in, 0, -1):
        cols.append(df.shift(i))
    # forecast sequence (t, t+1, ... t+n)
    for i in range(0, n_out):
        cols.append(df.shift(-i))
    # put it all together
    agg = pd.concat(cols, axis=1)
    # drop rows with NaN values
    if dropnan:
        agg.dropna(inplace=True)
    return agg.values

def train_test_split(data, n_test):
    return data[:-n_test, :], data[-n_test:, :]

def xgboost_forecast(train, testX):
    # transform list into array
    train = np.asarray(train)
    # split into input and output columns
    trainX, trainy = train[:, :-1], train[:, -1]
    print(trainX.shape)
    # fit model
    model = XGBRegressor(objective='reg:squarederror', n_estimators=1000)
    model.fit(trainX, trainy)
    # make a one-step prediction
    yhat = model.predict(np.asarray([testX]))
    return yhat[0]

def walk_forward_validation(tdata, n_test):
    predictions = list()
    
    train, test = train_test_split(tdata, n_test)
    # seed history with training dataset
    history = [x for x in train]
    # step over each time-step in the test set
    for i in range(len(test)):
        # split test row into input and output columns
        testX, testy = test[i, :-1], test[i, -1]
        # fit model on history and make a prediction
        yhat = xgboost_forecast(history, testX)
        # store forecast in list of predictions
        predictions.append(yhat)
        # add actual observation to history for the next loop
        history.append(test[i])
        # summarize progress
        print('>expected=%.1f, predicted=%.1f' % (testy, yhat))
    # estimate prediction error
    error = mean_absolute_error(test[:, -1], predictions)
    return error, test[:, -1], predictions

tdata = series_to_supervised(list(df['seq'].values), n_in=6)

mae, y, yhat = walk_forward_validation(tdata, 20)

How can I modify the program to work for multivariate time series analysis? is it just the series to supervised function that needs to be modified or changes are required elsewhere aswell?