<jupyter_start><jupyter_text>Cross Correlation Test<jupyter_code>np.corrcoef(fln_residual, joi_residual)
from sklearn.metrics import mean_squared_error
from math import sqrt

nrmse = sqrt(mean_squared_error(hour_temp['glo_avg'], fln_clean)) / np.mean(hour_temp['glo_avg'])
nrmse<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10714285714285714, 'fraction_numerical': 0.0, 'mean_word_length': 11.875, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '183465', 'license': 'MIT', 'n_tokens_mistral': 130, 'n_tokens_neox': 119, 'n_words': 17, 'path': 'notebooks/Basic/Correlation Test.ipynb', 'repo_name': 'cseveriano/spatio-temporal-forecasting', 'size': '308'}