# Time-Varying Regression Coefficients via Gaussian Random Walk in PyMC

In this notebook we want to illustrate how to use PyMC to fit a time-varying coefficient regression model. The motivation comes from post Exploring Tools for Interpretable Machine Learning where we studied a time series problem, regarding the prediction of the number of bike rentals, from a machine learning perspective. Concretely, we fitted and compared two machine learning models: a linear regression with interactions and a gradient boost model (XGBoost). The models regressors were mainly meteorological data and seasonality features. One interesting feature we saw, through PDP and ICE plots was that the temperature feature had a non-constant effect over the bike rentals (see here). Indeed, when the temperature is high (more than 25 degrees approximately), the bike rentals are negatively impacted by the temperature (to be fair, this is when controlling by other regressors) on average. What we want to do in this notebook is to tackle the same problem from a different perspective. Namely, use to use a GaussianRandomWalk to model the interaction effect between the temperature and the bike rentals. We of course start with the simple regression baseline for comparison.

## Prepare Notebook

import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm
from pymc.distributions.continuous import Exponential
from sklearn.preprocessing import MinMaxScaler
import seaborn as sns

plt.style.use("bmh")
plt.rcParams["figure.figsize"] = [10, 6]
plt.rcParams["figure.dpi"] = 100
plt.rcParams["figure.facecolor"] = "white"

%config InlineBackend.figure_format = "retina"
seed: int = sum(map(ord, "bikes"))
rng: np.random.Generator = np.random.default_rng(seed=seed)

For a detailed description of the data set please see here (from the book Interpretable Machine Learning:A Guide for Making Black Box Models Explainable by Christoph Molnar). For a detailed EDA see the previous post Exploring Tools for Interpretable Machine Learning.

data_path = "https://raw.githubusercontent.com/christophM/interpretable-ml-book/master/data/bike.csv"

data_path,
dtype={
"season": "category",
"mnth": "category",
"holiday": "category",
"weekday": "category",
"workingday": "category",
"weathersit": "category",
"cnt": "int",
},
)


season yr mnth holiday weekday workingday weathersit temp hum windspeed cnt days_since_2011
0 WINTER 2011 JAN NO HOLIDAY SAT NO WORKING DAY MISTY 8.175849 80.5833 10.749882 985 0
1 WINTER 2011 JAN NO HOLIDAY SUN NO WORKING DAY MISTY 9.083466 69.6087 16.652113 801 1
2 WINTER 2011 JAN NO HOLIDAY MON WORKING DAY GOOD 1.229108 43.7273 16.636703 1349 2
3 WINTER 2011 JAN NO HOLIDAY TUE WORKING DAY GOOD 1.400000 59.0435 10.739832 1562 3
4 WINTER 2011 JAN NO HOLIDAY WED WORKING DAY GOOD 2.666979 43.6957 12.522300 1600 4

## Data Formatting

data_df = raw_data_df.copy()

data_df["date"] = pd.to_datetime("2011-01-01") + data_df["days_since_2011"].apply(
lambda z: pd.Timedelta(z, unit="D")
)

data_df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 731 entries, 0 to 730
Data columns (total 13 columns):
#   Column           Non-Null Count  Dtype
---  ------           --------------  -----
0   season           731 non-null    category
1   yr               731 non-null    int64
2   mnth             731 non-null    category
3   holiday          731 non-null    category
4   weekday          731 non-null    category
5   workingday       731 non-null    category
6   weathersit       731 non-null    category
7   temp             731 non-null    float64
8   hum              731 non-null    float64
9   windspeed        731 non-null    float64
10  cnt              731 non-null    int64
11  days_since_2011  731 non-null    int64
12  date             731 non-null    datetime64[ns]
dtypes: category(6), datetime64[ns](1), float64(3), int64(3)
memory usage: 45.6 KB

Let’s plot the time development of the bike rentals and temperature over time.

fig, ax = plt.subplots(
nrows=2, ncols=1, figsize=(10, 6), sharex=True, sharey=False, layout="constrained"
)
sns.lineplot(x="date", y="cnt", data=data_df, color="black", ax=ax[0])
sns.lineplot(x="date", y="temp", data=data_df, color="C0", ax=ax[1])
ax[0].set(title="Count of bikes")
ax[1].set(title="Temperature")

We add a Gaussian bump function to model the end-of-year seasonality.

is_december = data_df["date"].dt.month == 12
eoy_mu = 25
eoy_sigma = 7
eoy_arg = (data_df["date"].dt.day - eoy_mu) / eoy_sigma
data_df["eoy"] = is_december * np.exp(-(eoy_arg**2))

fig, ax = plt.subplots()
sns.lineplot(x="date", y="eoy", data=data_df, color="C4", ax=ax)
ax.set(title="End-of-Year Gaussian Bump")

As we are going to fit a linear model is always good to scale the data.

target = "cnt"
target_scaled = f"{target}_scaled"

endog_scaler = MinMaxScaler()
exog_scaler = MinMaxScaler()

data_df[target_scaled] = endog_scaler.fit_transform(X=data_df[[target]])
data_df[["temp_scaled", "hum_scaled", "windspeed_scaled"]] = exog_scaler.fit_transform(
X=data_df[["temp", "hum", "windspeed"]]
)

Finally, we extract the features we want to include in the model.

n = data_df.shape[0]
# target
cnt = data_df[target].to_numpy()
cnt_scaled = data_df[target_scaled].to_numpy()
# date feature
date = data_df["date"].to_numpy()
# model regressors
temp_scaled = data_df["temp_scaled"].to_numpy()
hum_scaled = data_df["hum_scaled"].to_numpy()
windspeed_scaled = data_df["windspeed_scaled"].to_numpy()
holiday_idx, holiday = data_df["holiday"].factorize(sort=True)
workingday_idx, workingday = data_df["workingday"].factorize(sort=True)
weathersit_idx, weathersit = data_df["weathersit"].factorize(sort=True)
t = data_df["days_since_2011"].to_numpy() / data_df["days_since_2011"].max()
eoy = data_df["eoy"].to_numpy()

## Base Model

Before we jump into the time-varying coefficient model let us fist fit a baseline regression model. Let us follow the bayesian workflow for model development.

### 1. Model Specification

In a first step (after EDA and data pre-processing) we define the model structure. In particular we choose

1. Prior distributions for the regression coefficients and the noise.
2. Model parametrization, i.e. the structure of the linear model. Note that for the categorical variables we use a ZeroSumNormal since we are adding an intercept term.
3. Likelihood function, which is this case we decide for a StudentT distribution.
coords = {
"date": date,
"workingday": workingday,
"weathersit": weathersit,
}

with pm.Model(coords=coords) as base_model:
# --- priors ---
intercept = pm.Normal(name="intercept", mu=0, sigma=2)
b_temp = pm.Normal(name="b_temp", mu=0, sigma=1)
b_hum = pm.Normal(name="b_hum", mu=0, sigma=1)
b_windspeed = pm.Normal(name="b_windspeed", mu=0, sigma=1)
b_holiday = pm.ZeroSumNormal(name="b_holiday", sigma=1, dims="holiday")
b_workingday = pm.ZeroSumNormal(name="b_workingday", sigma=1, dims="workingday")
b_weathersit = pm.ZeroSumNormal(name="b_weathersit", sigma=1, dims="weathersit")
b_t = pm.Normal(name="b_t", mu=0, sigma=3)
b_eoy = pm.Normal(name="b_eoy", mu=0, sigma=1)
nu = pm.Gamma(name="nu", alpha=8, beta=2)
sigma = pm.HalfNormal(name="sigma", sigma=1)

# --- model parametrization ---
mu = pm.Deterministic(
name="mu",
var=(
intercept
+ b_t * t
+ b_temp * temp_scaled
+ b_hum * hum_scaled
+ b_windspeed * windspeed_scaled
+ b_holiday[holiday_idx]
+ b_workingday[workingday_idx]
+ b_weathersit[weathersit_idx]
+ b_eoy * eoy
),
dims="date",
)

# --- likelihood ---
likelihood = pm.StudentT(
name="likelihood", mu=mu, nu=nu, sigma=sigma, dims="date", observed=cnt_scaled
)

pm.model_to_graphviz(base_model)


### 2. Prior Predictive Analysis

We can analyze what the model expects before seeing the data.

with base_model:
# --- prior samples ---
prior_predictive_base = pm.sample_prior_predictive(samples=200, random_seed=rng)

palette = "viridis_r"
cmap = plt.get_cmap(palette)
percs = np.linspace(51, 99, 100)
colors = (percs - np.min(percs)) / (np.max(percs) - np.min(percs))

fig, ax = plt.subplots(figsize=(12, 6))

for i, p in enumerate(percs[::-1]):
upper = np.percentile(
prior_predictive_base.prior_predictive["likelihood"], p, axis=1
)
lower = np.percentile(
prior_predictive_base.prior_predictive["likelihood"], 100 - p, axis=1
)
color_val = colors[i]
ax.fill_between(
x=date,
y1=upper.flatten(),
y2=lower.flatten(),
color=cmap(color_val),
alpha=0.1,
)
sns.lineplot(x=date, y=cnt_scaled, color="black", ax=ax)
ax.set(
title="Base Model - Prior Predictive Samples",
xlabel="date",
ylabel=target_scaled,
)


The chosen prior distributions are not very restrictive but they image of the predictions is still within a reasonable range.

### 3. Model Fit

We sample from the posterior distributions using the JAX based NUTS sampler from Numpyro.

with base_model:
idata_base = pm.sample(
target_accept=0.9,
draws=2_000,
chains=5,
nuts_sampler="numpyro",
idata_kwargs={"log_likelihood": True},
random_seed=rng,
)
posterior_predictive_base = pm.sample_posterior_predictive(
trace=idata, random_seed=rng
)

# get number of divergences
idata_base["sample_stats"]["diverging"].sum().item()
0

### 4. Model Diagnostics

Now wee look into some diagnostics metrics an plots.

az.summary(data=idata_base, var_names=["~mu"])

mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk ess_tail r_hat
intercept 0.096 0.031 0.039 0.155 0.000 0.000 4774.0 5867.0 1.0
b_temp 0.474 0.016 0.442 0.503 0.000 0.000 10817.0 7829.0 1.0
b_hum -0.155 0.032 -0.216 -0.096 0.000 0.000 6113.0 6706.0 1.0
b_windspeed -0.129 0.023 -0.173 -0.085 0.000 0.000 7584.0 6548.0 1.0
b_t 0.460 0.012 0.437 0.483 0.000 0.000 10465.0 7553.0 1.0
b_eoy -0.310 0.025 -0.357 -0.262 0.000 0.000 10741.0 7346.0 1.0
b_holiday[HOLIDAY] -0.025 0.011 -0.045 -0.005 0.000 0.000 9671.0 7367.0 1.0
b_holiday[NO HOLIDAY] 0.025 0.011 0.005 0.045 0.000 0.000 9671.0 7367.0 1.0
b_workingday[NO WORKING DAY] -0.007 0.004 -0.014 0.000 0.000 0.000 12154.0 7185.0 1.0
b_workingday[WORKING DAY] 0.007 0.004 -0.000 0.014 0.000 0.000 12154.0 7185.0 1.0
b_weathersit[GOOD] 0.086 0.009 0.068 0.103 0.000 0.000 5353.0 6721.0 1.0
b_weathersit[MISTY] 0.044 0.008 0.029 0.060 0.000 0.000 8161.0 7154.0 1.0
b_weathersit[RAIN/SNOW/STORM] -0.130 0.015 -0.160 -0.102 0.000 0.000 5753.0 6144.0 1.0
nu 4.816 0.812 3.363 6.345 0.009 0.006 8752.0 7263.0 1.0
sigma 0.077 0.003 0.070 0.084 0.000 0.000 8498.0 7483.0 1.0
axes = az.plot_trace(
data=idata_base,
var_names=["~mu"],
compact=True,
kind="rank_bars",
backend_kwargs={"figsize": (12, 15), "layout": "constrained"},
)
plt.gcf().suptitle("Base Model - Trace", fontsize=16)

Overall, the model looks good.

axes = az.plot_forest(
data=idata_base,
var_names=["~mu", "~nu", "~sigma"],
combined=True,
r_hat=True,
ess=True,
figsize=(10, 6),
)
axes[0].axvline(x=0.0, color="black", linestyle="--")
plt.gcf().suptitle("Base Model - Posterior Distributions", fontsize=16)


Note that in this base model the temperature feature has a positive effect on bike rentals on average.

### 5. Posterior Predictive Distribution

Finally, let us take a look at the fitted values.

palette = "viridis_r"
cmap = plt.get_cmap(palette)
percs = np.linspace(51, 99, 100)
colors = (percs - np.min(percs)) / (np.max(percs) - np.min(percs))

posterior_predictive_likelihood = posterior_predictive_base.posterior_predictive[
"likelihood"
].stack(sample=("chain", "draw"))

posterior_predictive_likelihood_inv = endog_scaler.inverse_transform(
X=posterior_predictive_likelihood.to_numpy()
)

fig, ax = plt.subplots(figsize=(12, 6))

for i, p in enumerate(percs[::-1]):
upper = np.percentile(posterior_predictive_likelihood_inv, p, axis=1)
lower = np.percentile(posterior_predictive_likelihood_inv, 100 - p, axis=1)
color_val = colors[i]
ax.fill_between(
x=date,
y1=upper,
y2=lower,
color=cmap(color_val),
alpha=0.1,
)

sns.lineplot(
x=date,
y=posterior_predictive_likelihood_inv.mean(axis=1),
color="C2",
label="posterior predictive mean",
ax=ax,
)
sns.lineplot(
x=date,
y=cnt,
color="black",
label=target,
ax=ax,
)
ax.legend(loc="upper left")
ax.set(
title="Base Model - Posterior Predictive Samples",
xlabel="date",
ylabel=target,
)

Observe that for certain points in July the predictions and the fit do not coincide and go in opposite directions. This will be solved by adding the time-varying coefficient to the linear model for the temperature regressor (see below).

Remark: Note that the model predict negative values for the bike rentals. This is of course not good! An alterative choice of likelihood to model count data would resolve this (e.g. Poisson or Negative Binomial likelihoods).

## Time-Varying Coefficients Model

We now follow the same workflow above. The main difference is that we replace the regression coefficient of the temperature with GaussianRandomWalk and remove the intercept term.

### 1. Model Specification

with pm.Model(coords=coords) as model:
# --- priors ---
b_hum = pm.Normal(name="b_hum", mu=0, sigma=1)
b_windspeed = pm.Normal(name="b_windspeed", mu=0, sigma=1)
b_holiday = pm.ZeroSumNormal(name="b_holiday", sigma=1, dims="holiday")
b_workingday = pm.ZeroSumNormal(name="b_workingday", sigma=1, dims="workingday")
b_weathersit = pm.ZeroSumNormal(name="b_weathersit", sigma=1, dims="weathersit")
b_t = pm.Normal(name="b_t", mu=0, sigma=2)
b_eoy = pm.Normal(name="b_eoy", mu=0, sigma=1)
sigma_slopes = pm.Exponential(name="sigma_slope", lam=1 / 0.2)
nu = pm.Gamma(name="nu", alpha=8, beta=2)
sigma = pm.HalfNormal(name="sigma", sigma=1)

# --- model parametrization ---
slopes = pm.GaussianRandomWalk(
name="slopes",
sigma=sigma_slopes,
init_dist=Exponential.dist(lam=1 / 0.1),
dims="date",
)
mu = pm.Deterministic(
name="mu",
var=(
b_t * t
+ slopes * temp_scaled
+ b_hum * hum_scaled
+ b_windspeed * windspeed_scaled
+ b_holiday[holiday_idx]
+ b_workingday[workingday_idx]
+ b_weathersit[weathersit_idx]
+ b_eoy * eoy
),
dims="date",
)

# --- likelihood ---
likelihood = pm.StudentT(
name="likelihood", mu=mu, nu=nu, sigma=sigma, dims="date", observed=cnt_scaled
)

pm.model_to_graphviz(model)

### 2. Prior Predictive Analysis

with model:
# --- prior samples ---
prior_predictive = pm.sample_prior_predictive(samples=200, random_seed=rng)

palette = "viridis_r"
cmap = plt.get_cmap(palette)
percs = np.linspace(51, 99, 100)
colors = (percs - np.min(percs)) / (np.max(percs) - np.min(percs))

fig, ax = plt.subplots(figsize=(12, 6))

for i, p in enumerate(percs[::-1]):
upper = np.percentile(prior_predictive.prior_predictive["likelihood"], p, axis=1)
lower = np.percentile(
prior_predictive.prior_predictive["likelihood"], 100 - p, axis=1
)
color_val = colors[i]
ax.fill_between(
x=date,
y1=upper.flatten(),
y2=lower.flatten(),
color=cmap(color_val),
alpha=0.1,
)
sns.lineplot(x=date, y=cnt_scaled, color="black", ax=ax)
ax.set(
title="Time-Varying Coefficient Model - Prior Predictive Samples",
xlabel="date",
ylabel=target_scaled,
)

### 3. Model Fit

with model:
idata = pm.sample(
target_accept=0.97,
draws=2_000,
chains=5,
nuts_sampler="numpyro",
idata_kwargs={"log_likelihood": True},
random_seed=rng,
)
posterior_predictive = pm.sample_posterior_predictive(
trace=idata, random_seed=rng
)
# get number of divergences
idata["sample_stats"]["diverging"].sum().item()

13

We get a small number of divergences 😔 … still not dramatic.

### 4. Model Diagnostics

az.summary(data=idata, var_names=["~mu", "~slopes"])
mean sd hdi_3% hdi_97% mcse_mean mcse_sd ess_bulk ess_tail r_hat
b_hum -0.077 0.019 -0.112 -0.040 0.001 0.001 656.0 2021.0 1.00
b_windspeed -0.082 0.016 -0.111 -0.052 0.000 0.000 2182.0 5512.0 1.00
b_t 0.399 0.033 0.336 0.459 0.004 0.003 87.0 424.0 1.06
b_eoy -0.248 0.037 -0.316 -0.175 0.001 0.001 1852.0 3498.0 1.00
b_holiday[HOLIDAY] -0.043 0.008 -0.057 -0.027 0.000 0.000 2628.0 6585.0 1.00
b_holiday[NO HOLIDAY] 0.043 0.008 0.027 0.057 0.000 0.000 2628.0 6585.0 1.00
b_workingday[NO WORKING DAY] -0.005 0.003 -0.010 -0.000 0.000 0.000 12229.0 9441.0 1.00
b_workingday[WORKING DAY] 0.005 0.003 0.000 0.010 0.000 0.000 12229.0 9441.0 1.00
b_weathersit[GOOD] 0.102 0.006 0.090 0.113 0.000 0.000 2059.0 5124.0 1.00
b_weathersit[MISTY] 0.049 0.007 0.037 0.062 0.000 0.000 5743.0 7402.0 1.00
b_weathersit[RAIN/SNOW/STORM] -0.151 0.011 -0.172 -0.129 0.000 0.000 2845.0 5447.0 1.00
sigma_slope 0.044 0.006 0.033 0.055 0.000 0.000 212.0 347.0 1.02
nu 2.564 0.349 1.925 3.204 0.006 0.004 3667.0 7415.0 1.00
sigma 0.042 0.003 0.035 0.048 0.000 0.000 811.0 2206.0 1.01
axes = az.plot_trace(
data=idata,
var_names=["~mu", "~slopes"],
compact=True,
kind="rank_bars",
backend_kwargs={"figsize": (12, 15), "layout": "constrained"},
)
plt.gcf().suptitle("Time-Varying Coefficient Model - Trace", fontsize=16)

Let us now compare the two models in a forest plot.

axes = az.plot_forest(
data=[idata_base, idata],
model_names=["base", "time-varying"],
var_names=["~mu", "~slopes", "~nu", "~sigma"],
combined=True,
r_hat=True,
ess=True,
figsize=(12, 7),
)
axes[0].axvline(x=0.0, color="black", linestyle="--")
plt.gcf().suptitle("Posterior Distributions", fontsize=16)


Overall, there is no major change in the estimated regression coefficients besides the trend component.

We can also use the az.compare method to compare the two models.

az.compare(compare_dict={"base": idata_base, "time-varying": idata})

rank elpd_loo p_loo elpd_diff weight se dse warning scale
time-varying 0 859.510979 190.554494 0.000000 0.924529 28.507826 0.000000 False log
base 1 668.611994 11.713275 190.898985 0.075471 24.952297 21.054906 False log

It seems that the time-varying coefficient model is better at predicting the bike rentals.

### 5. Posterior Predictive Distribution

palette = "viridis_r"
cmap = plt.get_cmap(palette)
percs = np.linspace(51, 99, 100)
colors = (percs - np.min(percs)) / (np.max(percs) - np.min(percs))

posterior_predictive_likelihood = posterior_predictive.posterior_predictive[
"likelihood"
].stack(sample=("chain", "draw"))

posterior_predictive_likelihood_inv = endog_scaler.inverse_transform(
X=posterior_predictive_likelihood.to_numpy()
)

fig, ax = plt.subplots(figsize=(12, 6))

for i, p in enumerate(percs[::-1]):
upper = np.percentile(posterior_predictive_likelihood_inv, p, axis=1)
lower = np.percentile(posterior_predictive_likelihood_inv, 100 - p, axis=1)
color_val = colors[i]
ax.fill_between(
x=date,
y1=upper,
y2=lower,
color=cmap(color_val),
alpha=0.1,
)

sns.lineplot(
x=date,
y=posterior_predictive_likelihood_inv.mean(axis=1),
color="C2",
label="posterior predictive mean",
ax=ax,
)
sns.lineplot(
x=date,
y=cnt,
color="black",
label=target,
ax=ax,
)
ax.legend(loc="upper left")
ax.set(
title="Time-Varying Coefficient Model - Posterior Predictive Samples",
xlabel="date",
ylabel=target,
)


### 6. Temperature Effect Deep-Dive

Next, we wan to compare the inferred temperature effect from both models. To begin with, let us compare the mean effect for both models.

base_tmp_mean = (
idata_base.posterior["b_temp"].stack(sample=("chain", "draw")).to_numpy().mean()
)
time_varying_tmp_mean = (
idata.posterior["slopes"].stack(sample=("chain", "draw")).to_numpy().mean()
)
print(
f"""
base model mean effect = {base_tmp_mean: 0.3f}
------------------------------------------
time-varying model mean effect  = {time_varying_tmp_mean: 0.3f}
------------------------------------------
"""
)
base model mean effect =  0.473
------------------------------------------
time-varying model mean effect  =  0.536
------------------------------------------

It seems that the effect of the time-varying coefficient model is higher. Still, this is just one statistic.It is always better to see the data. The following plot shows the effect as a function of the temperature.

fig, ax = plt.subplots(figsize=(12, 7))
sns.scatterplot(
x="temp",
y="slopes",
data=data_df.assign(slopes=idata.posterior["slopes"].mean(dim=["chain", "draw"])),
hue="mnth",
palette="tab20",
)
sns.lineplot(
x="temp",
y="b_temp",
data=data_df.assign(
b_temp=idata_base.posterior["b_temp"].mean(dim=["chain", "draw"]).to_numpy()
),
color="black",
label="base model",
ax=ax,
)
sns.rugplot(x=data_df["temp"], color="black", alpha=0.5, ax=ax)
ax.axhline(y=0.0, color="gray", linestyle="--")
ax.legend(loc="center left", bbox_to_anchor=(1, 0.5))
ax.set(title="Temperature Effect", xlabel="temp", ylabel="effect")

We see many interesting features from this plot:

• The time-varying coefficient model indeed finds a non constant effect of temperature on bike rentals.
• The effect of second model is always positive and starts decreasing at around $$15$$ degrees which is consistent with the results found in the previous post Exploring Tools for Interpretable Machine Learning.
• The lowest points of the effect are:
• December and January. I think this is some seasonality effect that is not captured by the model.
• During the summer months when the temperatures are very high, the effect decreases considerably.
• The variance of the time-varying effect decreases with the temperature.

Next, we plot the temperature effect as a function of time.

palette = "cividis_r"
cmap = plt.get_cmap(palette)
percs = np.linspace(51, 99, 100)
colors = (percs - np.min(percs)) / (np.max(percs) - np.min(percs))

posterior_predictive_slopes = idata.posterior["slopes"].stack(sample=("chain", "draw"))

fig, ax = plt.subplots(
nrows=2, ncols=1, figsize=(10, 6), sharex=True, sharey=False, layout="constrained"
)

for i, p in enumerate(percs[::-1]):
upper = np.percentile(posterior_predictive_slopes, p, axis=1)
lower = np.percentile(posterior_predictive_slopes, 100 - p, axis=1)
color_val = colors[i]
ax[0].fill_between(
x=date,
y1=upper,
y2=lower,
color=cmap(color_val),
alpha=0.1,
)

sns.lineplot(
x="date",
y="b_temp",
data=data_df.assign(
b_temp=idata_base.posterior["b_temp"]
.stack(sample=("chain", "draw"))
.mean()
.to_numpy()
),
color="black",
label="base model",
ax=ax[0],
)
ax[0].axhline(y=0.0, color="gray", linestyle="--")
ax[0].legend(loc="upper left")
ax[0].set(title="Temperature Effect")
sns.lineplot(x="date", y="temp", data=data_df, color="C0", ax=ax[1])
ax[1].set(title="Temperature")

In the time-varying coefficient model, the temperature effect oscillates around the constant effect of the baseline model. We do see that during the spring months the effect is higher than the constant effect of the baseline model. This is consistent with the previous plot.

We can transform the scale back into the original scale:

temp_effect_scaled_baseline = (
idata_base["posterior"]["b_temp"] * exog_scaler.scale_[0]
) / endog_scaler.scale_[0]

temp_effect_scaled = (
idata["posterior"]["slopes"] * exog_scaler.scale_[0]
) / endog_scaler.scale_[0]

fig, ax = plt.subplots(figsize=(12, 7))
az.plot_hdi(
x=data_df["temp"].to_numpy(),
y=temp_effect_scaled,
hdi_prob=0.94,
color="C0",
fill_kwargs={"alpha": 0.4, "label": "$94\%$ HDI"},
ax=ax,
)
az.plot_hdi(
x=data_df["temp"].to_numpy(),
y=temp_effect_scaled,
hdi_prob=0.5,
color="C0",
fill_kwargs={"alpha": 0.6, "label": "$50\%$ HDI"},
ax=ax,
)
ax.axhline(
y=temp_effect_scaled_baseline.mean(dim=["chain", "draw"]).item(),
color="C1",
linestyle="--",
label="base model posterior predictive mean",
)
sns.rugplot(x=data_df["temp"], color="black", alpha=0.5, ax=ax)
ax.axhline(y=0.0, color="gray", linestyle="--")
ax.legend(loc="upper right")
ax.set(
title="Temperature Coefficient (Original Scale) - Posterior Predictive",
xlabel="temp",
ylabel=None,
)


For temperatures lower than $$-2$$ the estimate is noisy and is probably leaking some seasonality effects.

Note that these results are compatible with the insights found on the previous post Exploring Tools for Interpretable Machine Learning. For example, take a look into the PDP and ICE Pots

and specifically here is are the same plots restricted to the month of July:

Here we see the negative effect of temperature on bike count for the summer months.

Remark: It would be interesting to compare these results with Orbit’s KTR model (see Media Effect Estimation with Orbit’s KTR Model)