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Sure thing! @José Morales y currently updating some stuff. He might be able to help.

Hi @Emre Varol, thanks for the reproducible example. The differences are coming from the changes in the default parameters of xgboost, the colab version is very old so it's hard to exactly pinpoint the main differences, however it seems that using the parameters that were the default in 0.9 reduces the differences. Running

xgb.XGBRegressor(objective='reg:squarederror').get_params()

on xgb 0.9 returns:

{'base_score': 0.5,
 'booster': 'gbtree',
 'colsample_bylevel': 1,
 'colsample_bynode': 1,
 'colsample_bytree': 1,
 'gamma': 0,
 'importance_type': 'gain',
 'learning_rate': 0.1,
 'max_delta_step': 0,
 'max_depth': 3,
 'min_child_weight': 1,
 'missing': None,
 'n_estimators': 100,
 'n_jobs': 1,
 'nthread': None,
 'objective': 'reg:squarederror',
 'random_state': 0,
 'reg_alpha': 0,
 'reg_lambda': 1,
 'scale_pos_weight': 1,
 'seed': None,
 'silent': None,
 'subsample': 1,
 'verbosity': 1}

so using the following in your

get_forecast

function with xgb 1.6 I get pretty much the same results:

import math
    params = {
        'base_score': 0.5,
        'booster': 'gbtree',
        'colsample_bylevel': 1,
        'colsample_bynode': 1,
        'colsample_bytree': 1,
        'gamma': 0,
        'importance_type': 'gain',
        'learning_rate': 0.1,
        'max_delta_step': 0,
        'max_depth': 3,
        'min_child_weight': 1,
        'missing': math.nan,
        'n_estimators': 100,
        'n_jobs': 1,
        'nthread': None,
        'objective': 'reg:squarederror',
        'random_state': 0,
        'reg_alpha': 0,
        'reg_lambda': 1,
        'scale_pos_weight': 1,
        'seed': None,
        'silent': None,
        'subsample': 1,
        'verbosity': 1
    }
    model = xgb.XGBRegressor(**params)

many thanks @José Morales