code: def test_2_time_series_classification_short_horizon_binary(self):
      df = pd.read_csv("tests/data/arrivals.csv")[:127]
      target = "Traffic"
      df[target] = df[target] > 100000
  
      train_idxs = np.random.rand(len(df)) < 0.8
      train = df[train_idxs]
      test = df[~train_idxs]
  
      predictor = predictor_from_problem(
          df,
          ProblemDefinition.from_dict(
              {
                  "target": target,
                  "time_aim": 80,
                  "anomaly_detection": False,
                  "timeseries_settings": {
                      "order_by": "T",
                      "use_previous_target": True,
                      "window": 5,
                  },
              }
          ),
      )
  
      predictor.learn(train)
      predictor.predict(test)
  
imports: import random
  from datetime import datetime
  from datetime import timedelta
  import unittest
  import numpy
  import pandas
  from typing import List
  from scipy import signal
  from sklearn.metrics import r2_score
  from lightwood.api.types import ProblemDefinition
  from tests.utils.timing import train_and_check_time_aim
  from sktime.forecasting.base import ForecastingHorizon
  from sktime.forecasting.statsforecast import StatsForecastAutoARIMA
  from dataprep_ml.splitters import stratify
  from lightwood.api.high_level import json_ai_from_problem, code_from_json_ai, predictor_from_code, predictor_from_problem, predictor_from_json_ai
  from lightwood.mixer.sktime import SkTime
label_desc: Use image node_id 5 for calling the TestTimeseries obj's underlying member method code with example usage: obj.test_2_time_series_classification_short_horizon_binary() without return types