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XGBoost Assertion error

Hi !
I was trying to re-run your excellent example forecast.ipynb in Google Colab and I am getting the above error.
I used Colab, as installing prophet, xgboost in windows isn't well supported.
What's the meaning of the error?
Kind regards,
Dimitri

MAPE Calculation may be Incorrect

Hey, I found this repo from the Medium article - thanks for posting the code!

Just one thing, the calculation for MAPE is incorrect:

mape.append(np.abs((a - f)/f))

should be

mape.append(np.abs((a - f)/a))

if

y = [1]
yhat = [2]

The result for true - forecast / forecast will be 50%
The result for true - forecast / actual will be 100%

https://www.forecastpro.com/Trends/forecasting101August2011.html

I have seen other people using the forecasted values as the denominator, is there a reason for this?

Recursive Strategy Bug

Hello.
First, I would like to thank you guys for this amazing example on applying Direct and Recursive Strategy to N Step Ahead Forecasting. I was taking a look on the recursive strategy and came upon a doubt regarding it's implementation, where I think there's a bug.

If you take a look at the picture above (you can find the math in this article), the recursive strategy is basically the 1-step-ahead direct strategy with a "feedback" (the value found at each iteration will be inserted on target array).

When you're doing this piece of code

new_point = fcasted_values[-1] if len(fcasted_values) > 0 else 0.0
target = target.append(pd.Series(index=[date], data=new_point))

You're actually inserting the first prediction (N=1) on the recursive strategy with 0.0 value, instead of actually finding the prediction (N=1) value. This will affect the lags used on the features matrix, since there will be a lag with an incorrect value in all prediction steps.

Below you can see the target and feature values for 3 iteractions after inserting 0.0 as the first prediction.

Iteraction 1

Features
                     hour  weekday  dayofyear  ...      lag_1      lag_8     lag_25
2020-01-12 20:00:00    20        6         12  ...  65.919495  80.427320  72.718000
2020-01-12 21:00:00    21        6         12  ...  34.952133  57.917430  33.341960
2020-01-12 22:00:00    22        6         12  ...  33.911217  56.941563  33.081734
2020-01-12 23:00:00    23        6         12  ...  33.244377  56.193405  33.683514
2020-01-13 00:00:00     0        0         13  ...  33.390755  53.786278  33.244377

Target
2020-01-12 20:00:00    34.952133
2020-01-12 21:00:00    33.911217
2020-01-12 22:00:00    33.244377
2020-01-12 23:00:00    33.390755
2020-01-13 00:00:00     0.000000

Iteraction 2

Features
                     hour  weekday  dayofyear  ...      lag_1      lag_8     lag_25
2020-01-12 21:00:00    21        6         12  ...  34.952133  57.917430  33.341960
2020-01-12 22:00:00    22        6         12  ...  33.911217  56.941563  33.081734
2020-01-12 23:00:00    23        6         12  ...  33.244377  56.193405  33.683514
2020-01-13 00:00:00     0        0         13  ...  33.390755  53.786278  33.244377
2020-01-13 01:00:00     1        0         13  ...   0.000000  59.202316  33.407020

Target
2020-01-12 21:00:00    33.911217
2020-01-12 22:00:00    33.244377
2020-01-12 23:00:00    33.390755
2020-01-13 00:00:00     0.000000
2020-01-13 01:00:00    34.342800

Iteraction 3

Features
                     hour  weekday  dayofyear  ...      lag_1      lag_8     lag_25
2020-01-12 22:00:00    22        6         12  ...  33.911217  56.941563  33.081734
2020-01-12 23:00:00    23        6         12  ...  33.244377  56.193405  33.683514
2020-01-13 00:00:00     0        0         13  ...  33.390755  53.786278  33.244377
2020-01-13 01:00:00     1        0         13  ...   0.000000  59.202316  33.407020
2020-01-13 02:00:00     2        0         13  ...  34.342800  68.944670  32.057076

Target
2020-01-12 22:00:00    33.244377
2020-01-12 23:00:00    33.390755
2020-01-13 00:00:00     0.000000
2020-01-13 01:00:00    34.342800
2020-01-13 02:00:00     2.395295

Also, I didn't understand why you used, on the recursive strategy, the trained model (which is returned either from the linear_model or xgboost_model functions) instead of the 1 Step Ahead model (which is used on the Direct Estrategy).

Does this make any sense or have I understand something wrong?

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