<jupyter_start><jupyter_text><jupyter_code>def what_is_installed():
    import pycaret
    from pycaret import show_versions
    show_versions()

try:
    what_is_installed()
except:
    !pip install pycaret-ts-alpha
    what_is_installed()
import numpy as np
import pandas as pd
from pycaret.datasets import get_data
from pycaret.time_series import TSForecastingExperiment
#### Exogenous variables ----
data = pd.DataFrame({'a': np.random.randn(200), 'b': np.random.randn(200), 'c': np.random.randn(200)})

#### Produce dependent variable based on exogenous variables ----
# NOTE: Only dependent on 'a' and 'c' but not 'b'
data['y'] = data['a'].shift(4) + data['c'].shift(8)
data.dropna(inplace=True)
data.shape
#### Create Time Series Forecasting Experiment ----
exp = TSForecastingExperiment()
global_plot_settings = {"renderer": "colab"}
exp.setup(data=data, target="y", seasonal_period=1, fh=8, fig_kwargs=global_plot_settings, session_id=42)
exp.plot_model(plot="acf")<jupyter_output><empty_output><jupyter_text>**Not much to go by in terms of forecasting y just by itself (without exogenous variables)**<jupyter_code>exp.plot_model(plot="ccf")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13983050847457626, 'fraction_numerical': 0.012711864406779662, 'mean_word_length': 6.245398773006135, 'pattern_counts': {'":': 1, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '668333', 'license': 'MIT', 'n_tokens_mistral': 425, 'n_tokens_neox': 399, 'n_words': 95, 'path': 'time_series/pycaret/pycaret_ts_ccf.ipynb', 'repo_name': 'ngupta23/medium_articles', 'size': '1180'}