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View Code? Open in Web Editor NEWJupyter notebook implementing time series forecasting of energy consumption data with different methods.
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remcap davidtnly meiker28 ceciliakimura emilywheaton tusharkumarsaxena yuchennrma nadcharin fdoperezi mrudhulraj anoop-phoenix inuyashacas wshbakenergy-ts-analysis's Issues
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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