Profiling skforecast¶

This document shows the profiling of the main classes, methods and functions available in skforecast. Understanding the bottlenecks will help to:

• Use it more efficiently
• Improve the code for future releases

Libraries and data¶

In [ ]:

# Libraries
# ==============================================================================
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import platform
import psutil

import sklearn
from sklearn.dummy import DummyRegressor
from sklearn.ensemble import HistGradientBoostingRegressor

import skforecast
from skforecast.recursive import ForecasterRecursive

%load_ext pyinstrument

# Libraries # ============================================================================== import time import numpy as np import pandas as pd import matplotlib.pyplot as plt import platform import psutil import sklearn from sklearn.dummy import DummyRegressor from sklearn.ensemble import HistGradientBoostingRegressor import skforecast from skforecast.recursive import ForecasterRecursive %load_ext pyinstrument

In [2]:

# Versions
# ==============================================================================
print(f"Python version      : {platform.python_version()}")
print(f"scikit-learn version: {sklearn.__version__}")
print(f"skforecast version  : {skforecast.__version__}")
print(f"pandas version      : {pd.__version__}")
print(f"numpy version       : {np.__version__}")
print(f"psutil version      : {psutil.__version__}")
print("")

# System information
# ==============================================================================
print(f"Machine type: {platform.machine()}")
print(f"Processor type: {platform.processor()}")
print(f"Platform type: {platform.platform()}")
print(f"Operating system: {platform.system()}")
print(f"Operating system release: {platform.release()}")
print(f"Operating system version: {platform.version()}")
print(f"Number of physical cores: {psutil.cpu_count(logical=False)}")
print(f"Number of logical cores: {psutil.cpu_count(logical=True)}")

# Versions # ============================================================================== print(f"Python version : {platform.python_version()}") print(f"scikit-learn version: {sklearn.__version__}") print(f"skforecast version : {skforecast.__version__}") print(f"pandas version : {pd.__version__}") print(f"numpy version : {np.__version__}") print(f"psutil version : {psutil.__version__}") print("") # System information # ============================================================================== print(f"Machine type: {platform.machine()}") print(f"Processor type: {platform.processor()}") print(f"Platform type: {platform.platform()}") print(f"Operating system: {platform.system()}") print(f"Operating system release: {platform.release()}") print(f"Operating system version: {platform.version()}") print(f"Number of physical cores: {psutil.cpu_count(logical=False)}") print(f"Number of logical cores: {psutil.cpu_count(logical=True)}")

Python version      : 3.14.3
scikit-learn version: 1.9.1
skforecast version  : 0.26.0
pandas version      : 2.3.3
numpy version       : 2.5.3
psutil version      : 7.2.2

Machine type: arm64
Processor type: arm
Platform type: macOS-27.0.1-arm64-arm-64bit-Mach-O
Operating system: Darwin
Operating system release: 27.0.0
Operating system version: Darwin Kernel Version 27.0.0: Tue Aug 11 21:02:59 PDT 2026; root:xnu-13432.1.9~1/RELEASE_ARM64_T8142
Number of physical cores: 10
Number of logical cores: 10

A time series of length 1000 with random values is created.

In [3]:

# Data
# ==============================================================================
np.random.seed(123)
n = 1_000
data = pd.Series(data = np.random.normal(size=n))

# Data # ============================================================================== np.random.seed(123) n = 1_000 data = pd.Series(data = np.random.normal(size=n))

Profiling fit¶

To isolate the training process of the estimator from the other parts of the code, a dummy estimator class is used.

In [4]:

%%pyinstrument

forecaster = ForecasterRecursive(
                 estimator = DummyRegressor(strategy='constant', constant=1.),
                 lags      = 24
             )

forecaster.fit(y=data)

%%pyinstrument forecaster = ForecasterRecursive( estimator = DummyRegressor(strategy='constant', constant=1.), lags = 24 ) forecaster.fit(y=data)

╭─────────────────────────────── IgnoredArgumentWarning ───────────────────────────────╮
│ The number of bins has been reduced from 10 to 1 due to duplicated edges. This       │
│ happens when the values used to compute the edges of the bins are highly             │
│ concentrated or contain many repeated values.                                        │
│                                                                                      │
│ Category : skforecast.exceptions.IgnoredArgumentWarning                              │
│ Location :                                                                           │
│ /opt/homebrew/Caskroom/miniconda/base/envs/skforecast_py14/lib/python3.14/site-packa │
│ ges/skforecast/preprocessing/_preprocessing.py:2455                                  │
│ Suppress : warnings.simplefilter('ignore', category=IgnoredArgumentWarning)          │
╰──────────────────────────────────────────────────────────────────────────────────────╯

Almost all of the time spent by fit is required by the create_train_X_y method.

In [5]:

%%pyinstrument

forecaster = ForecasterRecursive(
                 estimator = HistGradientBoostingRegressor(max_iter=10, random_state=123),
                 lags      = 24
             )

forecaster.fit(y=data)

%%pyinstrument forecaster = ForecasterRecursive( estimator = HistGradientBoostingRegressor(max_iter=10, random_state=123), lags = 24 ) forecaster.fit(y=data)

When training a forecaster with a real machine learning estimator, the time spent by create_train_X_y is negligible compared to the time needed by the fit method of the estimator. Therefore, improving the speed of create_train_X_y will not have much impact.

Profiling create_train_X_y¶

Understand how the create_train_X_y method is influenced by the length of the series and the number of lags.

In [6]:

# Profiling `create_train_X_y` for different length of series and number of lags
# ======================================================================================
series_length = np.linspace(1000, 1000000, num=5, dtype=int)
n_lags = [5, 10, 50, 100, 200]
results = {}

for lags in n_lags:
    execution_time = []
    forecaster = ForecasterRecursive(
                     estimator = DummyRegressor(strategy='constant', constant=1.),
                     lags      = lags
                 )

    for n in series_length:
        y = pd.Series(data = np.random.normal(size=n))
        tic = time.perf_counter()
        _ = forecaster.create_train_X_y(y=y)
        toc = time.perf_counter()
        execution_time.append(toc - tic)

    results[lags] = execution_time

results = pd.DataFrame(
              data = results,
              index = series_length
          )

results

# Profiling `create_train_X_y` for different length of series and number of lags # ====================================================================================== series_length = np.linspace(1000, 1000000, num=5, dtype=int) n_lags = [5, 10, 50, 100, 200] results = {} for lags in n_lags: execution_time = [] forecaster = ForecasterRecursive( estimator = DummyRegressor(strategy='constant', constant=1.), lags = lags ) for n in series_length: y = pd.Series(data = np.random.normal(size=n)) tic = time.perf_counter() _ = forecaster.create_train_X_y(y=y) toc = time.perf_counter() execution_time.append(toc - tic) results[lags] = execution_time results = pd.DataFrame( data = results, index = series_length ) results

Out[6]:

	5	10	50	100	200
1000	0.000319	0.000223	0.000289	0.000245	0.000267
250750	0.001374	0.001413	0.006199	0.009560	0.021286
500500	0.001723	0.002631	0.012455	0.024050	0.047958
750250	0.002937	0.003883	0.017470	0.035970	0.071971
1000000	0.003751	0.006750	0.023587	0.043057	0.096112

In [7]:

fig, ax = plt.subplots(figsize=(7, 4))
results.plot(ax=ax, marker='.')
ax.set_xlabel('length of series')
ax.set_ylabel('time (seconds)')
ax.set_title('Profiling create_train_X_y()')
ax.legend(title='number of lags');

fig, ax = plt.subplots(figsize=(7, 4)) results.plot(ax=ax, marker='.') ax.set_xlabel('length of series') ax.set_ylabel('time (seconds)') ax.set_title('Profiling create_train_X_y()') ax.legend(title='number of lags');

Profiling predict¶

In [8]:

forecaster = ForecasterRecursive(
                 estimator = DummyRegressor(strategy='constant', constant=1.),
                 lags      = 24
             )

forecaster.fit(y=data)

forecaster = ForecasterRecursive( estimator = DummyRegressor(strategy='constant', constant=1.), lags = 24 ) forecaster.fit(y=data)

╭─────────────────────────────── IgnoredArgumentWarning ───────────────────────────────╮
│ The number of bins has been reduced from 10 to 1 due to duplicated edges. This       │
│ happens when the values used to compute the edges of the bins are highly             │
│ concentrated or contain many repeated values.                                        │
│                                                                                      │
│ Category : skforecast.exceptions.IgnoredArgumentWarning                              │
│ Location :                                                                           │
│ /opt/homebrew/Caskroom/miniconda/base/envs/skforecast_py14/lib/python3.14/site-packa │
│ ges/skforecast/preprocessing/_preprocessing.py:2455                                  │
│ Suppress : warnings.simplefilter('ignore', category=IgnoredArgumentWarning)          │
╰──────────────────────────────────────────────────────────────────────────────────────╯

In [9]:

%%pyinstrument

_ = forecaster.predict(steps=1000)

%%pyinstrument _ = forecaster.predict(steps=1000)

In [10]:

forecaster = ForecasterRecursive(
                 estimator = HistGradientBoostingRegressor(max_iter=10, random_state=123),
                 lags      = 24
             )

forecaster.fit(y=data)

forecaster = ForecasterRecursive( estimator = HistGradientBoostingRegressor(max_iter=10, random_state=123), lags = 24 ) forecaster.fit(y=data)

In [11]:

%%pyinstrument

_ = forecaster.predict(steps=1000)

%%pyinstrument _ = forecaster.predict(steps=1000)

Inside the predict method, the append action is the most expensive but, similar to what happen with fit, it is negligible compared to the time need by the predict method of the estimator.