Truong Hoang
06/05/2024, 5:26 AMunfollowed import
. I have tried a few hacks, such as manually adding py.typed
files to the root of the libraries, or even ignoring errors coming from the libraries entirely, but still unable to solve all issues completely. Is there any plan to make all the libraries PEP-561 compliant in the future? Thanks!Ml Club
06/06/2024, 6:34 AMsergio lopez
06/10/2024, 3:14 AMScottfree Analytics LLC
06/10/2024, 1:46 PMThe Imperfect Perfectionista
06/11/2024, 1:47 AMmodel = StatsForecast(models=[ ADIDA(),
CrostonClassic(),
IMAPA(),
TSB(alpha_d=0.2, alpha_p=0.2)], freq='W', n_jobs=-1)
The training data contains the unique_id
, and y
columns as usual, but the ds
column contains consecutive Mondays, as I want my predictions to happen weekly, on Mondays. Basically, every Monday morning, I want to forecast for that Monday, and Tue, Wed, Thu, Fri, Sat and Sun.
But when I call the predict
method after model training, it still gives me the sales forecasts on Sundays.
Is there any parameter to change this default setting?The Imperfect Perfectionista
06/11/2024, 11:02 AMunique_id
, and y
where ds
gives daily data, what's the Nixtla recommended way to convert it into monthly data if I want to predict every month's sale for each unique_id
? I can apply apply normal dataframe groupby sum for month, year and unique_id
, but wondering if Nixla provides its own API for this kind of operation? Also, the models understand freq='M'
right?Ml Club
06/12/2024, 8:16 AMvirgilio espina
06/13/2024, 5:35 AMLuis Enrique Patiño
06/13/2024, 9:32 PMvirgilio espina
06/14/2024, 8:47 AMvirgilio espina
06/15/2024, 8:18 AMGR
06/16/2024, 12:45 PMGR
06/16/2024, 12:46 PMStephen Cox
06/16/2024, 11:48 PMWTL
06/18/2024, 3:28 PMR
and StatsForecast
but I have a question about performance. In brief, I'm seeing performance that is about 3-4x slower than R
when applying AutoARIMA across data windows of varying sizes. This pseudocode:
model = AutoARIMA(method="CSS-ML")
sf = StatsForecast(models=[model]l, freq="B")
for dataset in datasets:
model_start_time = time.time()
sf.fit(df=dataset)
print(f"Model Fit Time: {(time.time() - model_start_time) * 1000}msec")
... gives me approximately 75 msec per loop iteration (call to sf.fit
).
This is not bad, per se, but R
running the forecast
auto.arima
model completes the same task in 20 msec.
What surprises me is that I see the same performance of 75 msec per iteration if I initialize sf
within the loop like this:
for dataset in datasets:
model_start_time = time.time()
model = AutoARIMA(method="CSS-ML")
sf = StatsForecast(models=[model]l, freq="B")
sf.fit(df=dataset)
print(f"Model Fit Time: {(time.time() - model_start_time) * 1000}msec")
I was expecting to see a slowdown due to re-initializing the StatsForecast model and the initial Numba compilation step happening each time, as compared to just once.
Because I don't see any evidence of improvement due to Numba JIT compilation, I am wondering... am I missing something obvious that would make StatsForecast faster (ideally) or at least as fast a R
?WTL
06/19/2024, 11:41 PMWTL
06/19/2024, 11:42 PMWTL
06/19/2024, 11:42 PMMateo De La Roche
06/20/2024, 11:02 PMreinier
06/24/2024, 3:09 PMFaridun Mamadbekov
06/27/2024, 12:33 PMfit()
and training with cross_validation(refit=True, ..)
? In other words is it acceptable to run cross_validation(refit=True, ..)
instead of fit()
, because with refit=True
, the CV method is internally running fit()
already? I understand, in the case of cross_validation the tail of input data will be split into validation sets depending on the n_windows
, step_size
, etc. The question is more about the equivalence of the fit()
and training done inside cross_validation()
. Is it ok to omit the fit()
and go straight for cross_validation(refit=True)
if the goal is to evaluate a methods performance on data?Kayla Robinson
06/27/2024, 6:53 PMGR
07/02/2024, 4:18 AMAlex Niemi
07/08/2024, 6:30 PMBharath Vishal G
07/15/2024, 3:02 PMAfiq Johari
07/16/2024, 9:09 AMAfiq Johari
07/17/2024, 11:57 AMXubin Lou
07/18/2024, 4:15 PMJacob Levy Abitbol
07/19/2024, 11:03 AMRicardo Barros Lourenço
07/19/2024, 4:50 PM