code: def test_plt_compare_timeseries():
      date_list = pd.date_range(
          start=datetime.datetime(2018, 6, 1),
          periods=24 * 600,
          freq="H",
      ).tolist()
  
      df = pd.DataFrame({"ts": date_list})
      df1 = df.copy()
      df2 = df.copy()
  
      value_col = "y"
      df1[value_col] = np.random.normal(size=df1.shape[0])
      df2[value_col] = np.random.normal(size=df2.shape[0])
  
      plt_compare_timeseries(
          df_dict={"obs": df1, "forecast": df2},
          time_col="ts",
          value_col="y",
          start_time=datetime.datetime(2019, 9, 1),
          end_time=datetime.datetime(2019, 9, 10),
      )
  
      # custom legends
      plt_compare_timeseries(
          df_dict={"obs": df1, "forecast": df2},
          time_col="ts",
          value_col="y",
          legends_dict={
              "obs": "observed",
              "forecast": "silverkite forecast",
          },
          start_time=datetime.datetime(2019, 9, 1),
          end_time=datetime.datetime(2019, 9, 10),
      )
  
      # custom colors
      plt_compare_timeseries(
          df_dict={"obs": df1, "forecast": df2},
          time_col="ts",
          value_col="y",
          colors_dict={"obs": "red", "forecast": "green"},
          start_time=datetime.datetime(2019, 9, 1),
          end_time=datetime.datetime(2019, 9, 10),
      )
  
file_path: greykite/greykite/tests/common/viz/test_timeseries_plotting_mpl.py
imports: import datetime
  import math
  from functools import partial
  import matplotlib
  import numpy
  import pandas
  import scipy.stats
  from greykite.common.viz.timeseries_plotting_mpl import plt_compare_timeseries
  from greykite.common.viz.timeseries_plotting_mpl import plt_longterm_ts_agg
  from greykite.common.viz.timeseries_plotting_mpl import plt_overlay_long_df
  from greykite.common.viz.timeseries_plotting_mpl import plt_overlay_with_bands
label_desc: Use image node_id 1 for calling a global function with example usage: test_plt_compare_timeseries() without return types