Today, give a try to Techtonique web app, a tool designed to help you make informed, data-driven decisions using Mathematics, Statistics, Machine Learning, and Data Visualization. Here is a tutorial with audio, video, code, and slides: https://moudiki2.gumroad.com/l/nrhgb. 100 API requests are now (and forever) offered to every user every month, no matter the pricing tier.

2 days ago, I presented a Cythonized implementation of NGBoost. NGBoost is a probabilistic boosting algorithm that provides uncertainty estimates along with predictions. It works by fitting a base learner (like decision trees or linear models) to the negative gradient of a specified loss function, and was first introduced by Stanford Machine Learning Group in the paper “NGBoost: Natural Gradient Boosting for Probabilistic Prediction” by Duan et al. (2019).

In this post, we will explore how to use NGBoost, a powerful library for probabilistic forecasting, in conjunction with the nnetsauce and cybooster libraries to perform time series analysis with native uncertainty quantification. The difference with the previous post is that we will use the native uncertainty quantification capabilities of NGBoost.

!pip install git+https://github.com/Techtonique/nnetsauce.git

!pip install git+https://github.com/Techtonique/cybooster.git

https://docs.techtonique.net/cybooster/index.html

https://docs.techtonique.net/nnetsauce/index.html

1 - Python version

`ice_cream_vs_heater`

import nnetsauce as ns
import pandas as pd
import numpy as np
from cybooster import NGBRegressor, NGBClassifier, SkNGBRegressor
from sklearn.datasets import load_diabetes, fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer, load_iris, load_wine, load_digits
from sklearn.metrics import accuracy_score, mean_squared_error, root_mean_squared_error
from sklearn.linear_model import LinearRegression, Ridge, BayesianRidge
from sklearn.tree import ExtraTreeRegressor
from time import time


url = "https://raw.githubusercontent.com/Techtonique/"
url += "datasets/main/time_series/multivariate/"
url += "ice_cream_vs_heater.csv"
df_temp = pd.read_csv(url)
df_temp.index = pd.DatetimeIndex(df_temp.date)
# must have# first other difference
df_icecream = df_temp.drop(columns=['date']).diff().dropna()


regr = ns.MTS(obj=SkNGBRegressor(),
              lags=20,
              type_pi="gaussian",
              show_progress=True)
regr.fit(df_icecream, return_std=True)
preds = regr.predict(h=30) # Store prediction results
regr.plot()

100%|██████████| 2/2 [00:08<00:00,  4.38s/it]

image-title-here

`USAccDeaths`

url = "https://raw.githubusercontent.com/Techtonique/"
url += "datasets/main/time_series/univariate/"
url += "USAccDeaths.csv"

df_temp = pd.read_csv(url)
df_temp.index = pd.DatetimeIndex(df_temp.date)
# must have# first other difference
df = df_temp.drop(columns=['date'])


regr = ns.MTS(obj=SkNGBRegressor(),
              lags=20,
              type_pi="gaussian",
              show_progress=True)
regr.fit(df, return_std=True)
preds = regr.predict(h=30) # Store prediction results
regr.plot()

100%|██████████| 1/1 [00:01<00:00,  1.25s/it]

image-title-here

`nile`

url = "https://raw.githubusercontent.com/Techtonique/"
url += "datasets/main/time_series/univariate/"
url += "nile.csv"

df_temp = pd.read_csv(url)
df_temp.index = pd.DatetimeIndex(df_temp.date)
# must have# first other difference
df = df_temp.drop(columns=['date'])


regr = ns.MTS(obj=SkNGBRegressor(),
              lags=20,
              type_pi="gaussian",
              show_progress=True)
regr.fit(df, return_std=True)
preds = regr.predict(h=30) # Store prediction results
regr.plot()

100%|██████████| 1/1 [00:02<00:00,  2.36s/it]

image-title-here

from sklearn.linear_model import LinearRegression

url = "https://raw.githubusercontent.com/Techtonique/"
url += "datasets/main/time_series/univariate/"
url += "AirPassengers.csv"

df_temp = pd.read_csv(url)
df_temp.index = pd.DatetimeIndex(df_temp.date)
# must have# first other difference
df = df_temp.drop(columns=['date'])


regr = ns.MTS(obj=SkNGBRegressor(LinearRegression()),
              lags=20,
              type_pi="gaussian",
              show_progress=True)
regr.fit(df, return_std=True)
preds = regr.predict(h=30) # Store prediction results
regr.plot()

100%|██████████| 1/1 [00:01<00:00,  1.11s/it]

image-title-here

from sklearn.linear_model import Ridge

url = "https://raw.githubusercontent.com/Techtonique/"
url += "datasets/main/time_series/univariate/"
url += "a10.csv"

df_temp = pd.read_csv(url)
df_temp.index = pd.DatetimeIndex(df_temp.date)
# must have# first other difference
df = df_temp.drop(columns=['date'])


regr = ns.MTS(obj=SkNGBRegressor(Ridge()),
              lags=15,
              type_pi="gaussian",
              show_progress=True)
regr.fit(df, return_std=True)
preds = regr.predict(h=30) # Store prediction results
regr.plot()

100%|██████████| 1/1 [00:00<00:00,  1.01it/s]

image-title-here

2 - R version

%load_ext rpy2.ipython

%%R

install.packages("pak")
pak::pak("reticulate")

%%R

pak::pak(c("readr", "xts", "ggplot2"))

%%R


# Load necessary libraries
library(reticulate)
library(readr)
library(xts)
library(ggplot2)

# Import Python packages
ns <- import("nnetsauce")
cyb <- import("cybooster")
sklearn <- import("sklearn")


# Load the dataset
url <- "https://raw.githubusercontent.com/Techtonique/datasets/main/time_series/multivariate/ice_cream_vs_heater.csv"
df_temp <- read.csv(url)

%%R

head(df_temp)

        date heater icecream
2004-01-01     27       13
2004-02-01     18       15
2004-03-01     14       16
2004-04-01     13       19
2004-05-01     13       21
2004-06-01     13       24

%%R

np <- import("numpy")

# Assuming SkNGBRegressor is available in the sklearn R package or a similar implementation
# If not, you might need to use a different model or wrap the Python version
regr <- ns$MTS(obj = cyb$SkNGBRegressor(),
               lags = 20L,
               type_pi = "gaussian",
               show_progress = TRUE)

%%R

df <- df_temp[, -1]
rownames(df) <- df_temp$date

%%R

df

           heater icecream
2004-01-01     27       13
2004-02-01     18       15
2004-03-01     14       16
2004-04-01     13       19
2004-05-01     13       21
2004-06-01     13       24
2004-07-01     13       27
2004-08-01     14       20
2004-09-01     15       18
2004-10-01     20       15
2004-11-01     24       15
2004-12-01     29       14
2005-01-01     27       15
2005-02-01     17       15
2005-03-01     15       17
2005-04-01     14       19
2005-05-01     13       22
2005-06-01     13       28
2005-07-01     12       29
2005-08-01     13       21
2005-09-01     16       16
2005-10-01     25       14
2005-11-01     25       14
2005-12-01     31       14
2006-01-01     21       14
2006-02-01     20       15
2006-03-01     16       16
2006-04-01     14       19
2006-05-01     13       23
2006-06-01     13       27
2006-07-01     13       32
2006-08-01     13       24
2006-09-01     16       19
2006-10-01     22       16
2006-11-01     23       16
2006-12-01     25       17
2007-01-01     25       16
2007-02-01     23       17
2007-03-01     16       18
2007-04-01     14       20
2007-05-01     13       25
2007-06-01     13       30
2007-07-01     12       29
2007-08-01     12       23
2007-09-01     15       19
2007-10-01     20       15
2007-11-01     26       15
2007-12-01     29       16
2008-01-01     26       15
2008-02-01     20       17
2008-03-01     16       17
2008-04-01     15       20
2008-05-01     14       25
2008-06-01     14       28
2008-07-01     14       28
2008-08-01     14       23
2008-09-01     17       18
2008-10-01     26       15
2008-11-01     28       15
2008-12-01     31       14
2009-01-01     29       15
2009-02-01     21       17
2009-03-01     17       18
2009-04-01     15       22
2009-05-01     14       27
2009-06-01     14       32
2009-07-01     13       34
2009-08-01     13       30
2009-09-01     16       24
2009-10-01     24       19
2009-11-01     23       20
2009-12-01     33       18
2010-01-01     30       18
2010-02-01     22       19
2010-03-01     17       21
2010-04-01     15       23
2010-05-01     14       28
2010-06-01     12       30
2010-07-01     11       34
2010-08-01     12       28
2010-09-01     14       22
2010-10-01     21       18
2010-11-01     27       17
2010-12-01     32       16
2011-01-01     31       24
2011-02-01     24       24
2011-03-01     18       25
2011-04-01     15       45
2011-05-01     14       34
2011-06-01     14       41
2011-07-01     13       46
2011-08-01     14       35
2011-09-01     17       30
2011-10-01     25       30
2011-11-01     31       27
2011-12-01     32       29
2012-01-01     28       30
2012-02-01     21       30
2012-03-01     17       35
2012-04-01     15       39
2012-05-01     14       46
2012-06-01     13       53
2012-07-01     13       55
2012-08-01     13       41
2012-09-01     16       31
2012-10-01     25       24
2012-11-01     32       23
2012-12-01     29       23
2013-01-01     30       24
2013-02-01     23       25
2013-03-01     20       27
2013-04-01     16       31
2013-05-01     15       37
2013-06-01     14       44
2013-07-01     14       48
2013-08-01     14       37
2013-09-01     17       28
2013-10-01     27       22
2013-11-01     36       21
2013-12-01     39       21
2014-01-01     39       24
2014-02-01     28       24
2014-03-01     21       28
2014-04-01     17       32
2014-05-01     16       39
2014-06-01     15       45
2014-07-01     15       51
2014-08-01     16       40
2014-09-01     19       28
2014-10-01     26       23
2014-11-01     45       21
2014-12-01     32       22
2015-01-01     36       24
2015-02-01     32       26
2015-03-01     21       33
2015-04-01     17       40
2015-05-01     17       46
2015-06-01     17       49
2015-07-01     16       57
2015-08-01     17       45
2015-09-01     19       35
2015-10-01     29       27
2015-11-01     37       26
2015-12-01     35       25
2016-01-01     40       30
2016-02-01     28       32
2016-03-01     21       38
2016-04-01     20       45
2016-05-01     19       51
2016-06-01     18       61
2016-07-01     17       71
2016-08-01     17       52
2016-09-01     21       42
2016-10-01     29       39
2016-11-01     39       46
2016-12-01     52       66
2017-01-01     40       35
2017-02-01     27       39
2017-03-01     25       44
2017-04-01     20       55
2017-05-01     21       60
2017-06-01     20       74
2017-07-01     19       89
2017-08-01     19       64
2017-09-01     23       48
2017-10-01     33       40
2017-11-01     43       36
2017-12-01     56       35
2018-01-01     56       40
2018-02-01     33       42
2018-03-01     27       51
2018-04-01     24       56
2018-05-01     22       71
2018-06-01     21       79
2018-07-01     21       91
2018-08-01     21       66
2018-09-01     24       49
2018-10-01     39       39
2018-11-01     53       34
2018-12-01     48       36
2019-01-01     49       39
2019-02-01     39       42
2019-03-01     30       53
2019-04-01     24       57
2019-05-01     23       65
2019-06-01     22       82
2019-07-01     21      100
2019-08-01     21       68
2019-09-01     24       51
2019-10-01     40       40
2019-11-01     56       36
2019-12-01     46       36
2020-01-01     41       43
2020-02-01     34       45
2020-03-01     25       44
2020-04-01     25       53
2020-05-01     27       70
2020-06-01     24       74

%%R

# Fit the model
regr$fit(df)

100%|██████████| 2/2 [00:05<00:00,  2.66s/it]

MTS(lags=20, obj=SkNGBRegressor(), type_pi='gaussian')

%%R

library(ggplot2)

# Make predictions
preds <- regr$predict(h = 30L, return_std=TRUE)

# Plot the results
regr$plot("heater")
regr$plot("icecream")

image-title-here

%%R

preds

DescribeResult(mean=            heater  icecream
date                        
2020-07-01   22.07     93.22
2020-08-01   22.04     69.47
2020-09-01   23.94     54.68
2020-10-01   40.38     42.04
2020-11-01   52.47     39.01
2020-12-01   45.44     38.33
2021-01-01   42.34     41.62
2021-02-01   35.54     45.68
2021-03-01   25.94     45.46
2021-04-01   25.93     54.19
2021-05-01   27.34     69.47
2021-06-01   24.67     74.85
2021-07-01   22.86     93.39
2021-08-01   22.07     73.81
2021-09-01   23.86     52.58
2021-10-01   40.81     46.88
2021-11-01   51.47     46.63
2021-12-01   47.05     41.83
2022-01-01   42.96     42.51
2022-02-01   37.37     45.35
2022-03-01   30.64     44.62
2022-04-01   27.21     53.50
2022-05-01   27.05     69.65
2022-06-01   24.48     72.62
2022-07-01   22.68     91.98
2022-08-01   22.01     71.78
2022-09-01   23.78     54.59
2022-10-01   38.75     52.85
2022-11-01   48.41     54.60
2022-12-01   46.83     48.62, lower=            heater  icecream
date                        
2020-07-01   20.34     90.50
2020-08-01   20.31     66.75
2020-09-01   22.21     51.96
2020-10-01   38.65     39.32
2020-11-01   50.75     36.28
2020-12-01   43.71     35.61
2021-01-01   40.61     38.90
2021-02-01   33.81     42.96
2021-03-01   24.21     42.73
2021-04-01   24.20     51.47
2021-05-01   25.61     66.75
2021-06-01   22.95     72.13
2021-07-01   21.14     90.67
2021-08-01   20.34     71.09
2021-09-01   22.13     49.86
2021-10-01   39.09     44.16
2021-11-01   49.74     43.91
2021-12-01   45.33     39.11
2022-01-01   41.24     39.78
2022-02-01   35.64     42.63
2022-03-01   28.91     41.90
2022-04-01   25.48     50.77
2022-05-01   25.32     66.92
2022-06-01   22.76     69.90
2022-07-01   20.95     89.26
2022-08-01   20.28     69.06
2022-09-01   22.05     51.87
2022-10-01   37.02     50.13
2022-11-01   46.69     51.88
2022-12-01   45.10     45.90, upper=            heater  icecream
date                        
2020-07-01   23.80     95.94
2020-08-01   23.77     72.19
2020-09-01   25.67     57.40
2020-10-01   42.11     44.77
2020-11-01   54.20     41.73
2020-12-01   47.16     41.05
2021-01-01   44.06     44.35
2021-02-01   37.27     48.40
2021-03-01   27.67     48.18
2021-04-01   27.65     56.91
2021-05-01   29.07     72.19
2021-06-01   26.40     77.58
2021-07-01   24.59     96.12
2021-08-01   23.79     76.54
2021-09-01   25.58     55.31
2021-10-01   42.54     49.60
2021-11-01   53.20     49.36
2021-12-01   48.78     44.55
2022-01-01   44.69     45.23
2022-02-01   39.09     48.08
2022-03-01   32.36     47.34
2022-04-01   28.93     56.22
2022-05-01   28.78     72.37
2022-06-01   26.21     75.34
2022-07-01   24.40     94.70
2022-08-01   23.73     74.51
2022-09-01   25.50     57.32
2022-10-01   40.47     55.57
2022-11-01   50.14     57.33
2022-12-01   48.56     51.34)

Comments powered by Talkyard.

@misc{ tmoudiki20251008, author = { T. Moudiki }, title = { Native uncertainty quantification for time series with NGBoost }, url = { https://thierrymoudiki.github.io/blog/2025/10/08/r/python/ngboost-time-series }, year = { 2025 } }