Hey! As part of our #reinventforecasting week, we released the MSTL model. Show some love community ❤️ https://www.reddit.com/r/datascience/comments/z7etr8/on_the_law_of_similarity_or_how_10000yearold/

Hello and thank you. I like the MSTL model for my timeseries. Do you recommend adjusting any hyperparameters for the MSTL or AutoARIMA models? As noted in the example notebook, I am using MSTL and AutoARIMA as trend_forecaster and am hoping to enhance prediction accuracy. I tried AutoTheta, but it takes much longer than AutoARIMA and the predictions are nearly same. Thank you very much.

hey @Sai Avinash! Thanks for using our library. If your data exhibits a clear trend, you could use

RandomWalkWithDrift

as a trend forecaster. Trying

AutoETS

and

DynamicOptimizedTheta

would also be a good idea.

@fede (nixtla) (they/them), Thank you for the suggestions. I tried to use AutoETS but I couldn't able to get it to work. Please see the error below models = [MSTL( season_length=[24*7, 24*31], #trend_forecaster=AutoARIMA() trend_forecaster=AutoETS() )]

Exception                                 Traceback (most recent call last)
Cell In [474], line 1
----> 1 models = [MSTL(
      2     season_length=[24*7, 24*31],  #8
      3     #trend_forecaster=AutoARIMA(truncate=True)
      4     trend_forecaster=AutoETS()
      5 )]

File ~\miniconda3\envs\neuralforecast\lib\site-packages\statsforecast\models.py:2975, in MSTL.__init__(self, season_length, trend_forecaster)
   2973 if repr(trend_forecaster) == "AutoETS":
   2974     if trend_forecaster.model[2] != "N":
-> 2975         raise Exception(
   2976             "Trend forecaster should not adjust " "seasonal models."
   2977         )
   2978 if hasattr(trend_forecaster, "season_length"):
   2979     if trend_forecaster.season_length != 1:

Exception: Trend forecaster should not adjust seasonal models.

hey @Sai Avinash! Passing

AutoETS(model='ZZN')

should work. In the default setting

AutoETS

also adjust seasonal models, but in this case, we don’t want to model those patterns (they are already modeled by MSTL). Passing

model='ZZN'

tells

AutoETS

to omit the seasonal models.

Thank you for responding. I used model='ZZN' and it worked. Sorry if this has previously been covered in the docs; I'm new to NBEATS and this package. How do I make levels work with AutoETS? When I pass levels through sf.forecast, I get

ValueError: Columns must be same length as key

Yesterday we released a new version of StatsForecast, including prediction intervals for

AutoETS

. You can install the new version using

pip install statsforecast>=1.4.0

.

Thank you very much. I will update. Sorry for disturbing you; I'd also like to know if NBEATS may be used as a trend forecaster. I use NBEATS for my other timeseries and was wondering if the same could be done with MSTL

Currently, only StatsForecast models are supported as trend forecasters for MSTL. An interesting approach to improve performance might be to ensemble MSTL and NBEATS (or our NTHIS model). Here’s an example of how to use NHITS using the NeuralForecast library: https://nixtla.github.io/neuralforecast/examples/getting_started.html

Thank you very much. I'll give NHITS a shot. I appreciate everything you've done. Have a wonderful day.