Chapter 128 · Solutions
Forecasting Models · Solutions
Five challenges, each verified in code.
Forecasting Models · Challenge Solutions¶
Worked solutions to the five challenges from Chapter 128. Each uses the same airport-passenger series, the same chronological train/test split, and MAPE to compare forecasts.
In [1]:
import numpy as np, pandas as pd, matplotlib.pyplot as plt
import seaborn as sns # seaborn = high-level statistical plots (heatmaps, pairplots, count/bar plots)
from matplotlib.colors import ListedColormap
EM="#0284c7"; DEEP="#075985"; LIGHT="#bae6fd"; INK="#1a2138"; GRID="#e6e9f2"; RED="#ef4444"; AMBER="#d97706"; GREEN="#059669"; BLUE="#2563eb"; PUR="#9333ea"; GREY="#94a3b8"; SLATE="#475569"; ORG="#0284c7"; CYAN="#0891b2"
plt.rcParams.update({"figure.facecolor":"white","axes.facecolor":"white","figure.dpi":110,"font.size":11,
"axes.edgecolor":GRID,"axes.grid":True,"grid.color":GRID,"axes.axisbelow":True,"axes.spines.top":False,
"axes.spines.right":False,"axes.titlesize":12,"axes.titleweight":"bold","legend.frameon":False})
sns.set_style("whitegrid")
BASE="https://raw.githubusercontent.com/johnfisher-ai/Statistics-Data-Science-AI-Visual-Book/main/data/"
In [2]:
from statsmodels.tsa.holtwinters import SimpleExpSmoothing, ExponentialSmoothing, Holt
from statsmodels.tsa.statespace.sarimax import SARIMAX
import warnings; warnings.filterwarnings('ignore')
def mape(a,f): return float(np.mean(np.abs((a.values-np.asarray(f))/a.values))*100)
try: raw = pd.read_excel('../../data/forecasting-models--airport_passengers.xlsx', sheet_name='Data')
except FileNotFoundError: raw = pd.read_excel(BASE + 'forecasting-models--airport_passengers.xlsx', sheet_name='Data')
raw['month']=pd.to_datetime(raw['month']); s = raw.set_index('month')['passengers'].asfreq('MS')
train, test = s[:-12], s[-12:]; print('train', len(train), '| test', len(test))
train 108 | test 12
CHALLENGE 1
Baselines
Compute naive, seasonal-naive, and drift baselines; which is the bar to beat?
In [3]:
naive = np.repeat(train.iloc[-1], 12)
snaive = train.iloc[-12:].values
drift = train.iloc[-1] + (np.arange(1,13))*((train.iloc[-1]-train.iloc[0])/(len(train)-1))
for name,f in [('naive',naive),('seasonal-naive',snaive),('drift',drift)]: print(f'{name:16s} MAPE {mape(test,f):.2f}%')
naive MAPE 13.63% seasonal-naive MAPE 4.13% drift MAPE 15.15%
The seasonal-naive forecast (about 4.1%) is the one to beat here: it captures the yearly wave that the naive and drift baselines miss. Every serious model should clear it.
CHALLENGE 2
The exponential-smoothing ladder
Fit SES, Holt (trend), and Holt-Winters (trend+season); see what each added component buys you.
In [4]:
ses = SimpleExpSmoothing(train).fit()
holt = Holt(train).fit()
hw = ExponentialSmoothing(train, trend='add', seasonal='mul', seasonal_periods=12).fit()
for name,m in [('SES (level only)',ses),('Holt (+trend)',holt),('Holt-Winters (+season)',hw)]: print(f'{name:24s} MAPE {mape(test, m.forecast(12)):.2f}%')
SES (level only) MAPE 13.63% Holt (+trend) MAPE 14.32% Holt-Winters (+season) MAPE 2.83%
Each rung adds a component. SES tracks only the level (flat forecast). Holt adds a trend but, still blind to the yearly wave, can even do slightly worse by extrapolating a bare trend. Only Holt-Winters, which adds seasonality, captures both forces and wins decisively. The lesson: add the component your data actually has.
CHALLENGE 3
Choosing SARIMA orders
Fit two candidate orders and pick by AIC; forecast the winner with a 95% interval.
In [5]:
cands = [((1,1,1),(1,1,1,12)), ((0,1,1),(0,1,1,12))]
fits = {(o,so): SARIMAX(train, order=o, seasonal_order=so, enforce_stationarity=False, enforce_invertibility=False).fit(disp=False) for o,so in cands}
for k,m in fits.items(): print(f'SARIMA{k[0]}{k[1]} AIC {m.aic:.0f} MAPE {mape(test, m.forecast(12)):.2f}%')
best = min(fits.values(), key=lambda m: m.aic); fc = best.get_forecast(12); ci = fc.conf_int()
print('\nchosen by lowest AIC; 12-month forecast with 95% interval:')
print(pd.concat([fc.predicted_mean.round(0).rename('forecast'), ci.round(0)], axis=1).head(3))
SARIMA(1, 1, 1)(1, 1, 1, 12) AIC 1436 MAPE 3.74%
SARIMA(0, 1, 1)(0, 1, 1, 12) AIC 1440 MAPE 3.71%
chosen by lowest AIC; 12-month forecast with 95% interval:
forecast lower passengers upper passengers
2023-01-01 44828.0 41163.0 48493.0
2023-02-01 44120.0 40243.0 47996.0
2023-03-01 51089.0 47182.0 54996.0
AIC balances fit against complexity, so it is a fair way to pick orders without touching the test set. The chosen model produces both a point forecast and a 95% prediction interval, honest uncertainty that grows with the horizon.
CHALLENGE 4
Head to head
Compare Holt-Winters and SARIMA on the same holdout.
In [6]:
hw = ExponentialSmoothing(train, trend='add', seasonal='mul', seasonal_periods=12).fit()
sar = SARIMAX(train, order=(1,1,1), seasonal_order=(1,1,1,12), enforce_stationarity=False, enforce_invertibility=False).fit(disp=False)
res = pd.DataFrame({'model':['Holt-Winters (mul)','SARIMA(1,1,1)(1,1,1,12)'], 'MAPE_%':[round(mape(test, hw.forecast(12)),2), round(mape(test, sar.forecast(12)),2)]})
print(res.to_string(index=False))
fig,ax=plt.subplots(figsize=(9,4)); ax.plot(s.index[-24:], s.values[-24:], color=GREY, lw=2, label='actual')
ax.plot(test.index, hw.forecast(12), 'o--', color=EM, label='Holt-Winters'); ax.plot(test.index, sar.forecast(12), 's--', color=PUR, label='SARIMA')
ax.set(title='Holt-Winters vs SARIMA on the holdout', ylabel='passengers'); ax.legend(); plt.tight_layout(); plt.show()
model MAPE_%
Holt-Winters (mul) 2.83
SARIMA(1,1,1)(1,1,1,12) 3.74
Both clear the baseline; Holt-Winters edges SARIMA on this multiplicative series. There is no universal winner, the holdout decides, and it is cheap to fit both.
CHALLENGE 5
Static vs dynamic, and the horizon
Show that multi-step (dynamic) error grows the further out you forecast.
In [7]:
sar = SARIMAX(train, order=(1,1,1), seasonal_order=(1,1,1,12), enforce_stationarity=False, enforce_invertibility=False).fit(disp=False)
fc = sar.forecast(12); errs = np.abs((test.values - fc.values)/test.values)*100
fig,ax=plt.subplots(figsize=(9,3.8)); ax.bar(range(1,13), errs, color=EM); ax.set(title='Multi-step forecast error grows with the horizon', xlabel='months ahead', ylabel='abs % error')
plt.tight_layout(); plt.show()
print('error at 1 month ahead: %.1f%% | at 12 months ahead: %.1f%%' % (errs[0], errs[-1]))
print('a static one-step evaluation would hide this growth -> match the evaluation horizon to the decision')
error at 1 month ahead: 2.2% | at 12 months ahead: 3.4% a static one-step evaluation would hide this growth -> match the evaluation horizon to the decision
A dynamic forecast compounds its own errors as it rolls forward, so the further out you look, the wider the miss. Evaluating only one step ahead (static) hides that growth. Always test at the horizon you will actually forecast.
Statistics, Data Science and AI: A Visual Handbook · © 2026 John Fisher