def METHOD_NAME(args):
    quantiles = args.quantiles.split(",")
    model = ForecastingEngine(
        model_path=args.model,
        input_name=args.input_name,
        output_name=args.output_name,
        quantiles=quantiles
    )
    dataset = ForecastingDataset(args.input)
    fig, ax = plt.subplots()
    fig.canvas.manager.set_window_title('time_series_forecasting')
    ax.get_yaxis().set_visible(False)
    actors = ForecastingActors(
        ax=ax,
        quantiles=quantiles,
        total_time=dataset.get_total_time()
    )
    def animate(idx):
        sample = dataset[idx]
        preds = model(sample.inputs)
        curves = actors.update_curves(preds, sample)
        ax.relim()
        ax.autoscale_view(True, True, True)
        return curves
    _ = animation.FuncAnimation(
        fig,
        animate,
        frames=len(dataset),
        interval=50,
        blit=True
    )
    ax.legend(loc="upper right")
    plt.show()