Business Forecasting is one of the applications of Time Series Forecasting. In Business Forecasting, we aim to forecast future sales, expenditure, or revenue by using the historical Time Series data generated by the business. So, if you want to learn how to perform business forecasting, this article is for you. In this article, I will take you through the task of Business Forecasting using Python.
Why does a Business Needs Business Forecasting?
Every business is looking for strategies to improve its profits. Data science professionals play a major role in providing the most accurate predictions for any given time. The data generated by a company is always handy for analyzing the future behaviour of target customers. By predicting future business trends, a business can make better decisions to improve its future performance.
I hope you have understood why a business today needs to use business forecasting techniques. Forecasting sales, revenue or expenditure are some use cases of business forecasting. So, in the section below, I will take you through a task of business forecasting where we will aim to predict the quarterly revenue of Adidas. The data I am using for this task is collected manually from quarterly sales reports from Adidas. You can download the dataset from here.
Business Forecasting using Python
Let’s get started with the task of business forecasting by importing the necessary Python libraries and the dataset:
import pandas as pd
from datetime import date, timedelta
import datetime
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
from statsmodels.tsa.seasonal import seasonal_decompose
from statsmodels.graphics.tsaplots import plot_pacf
from statsmodels.tsa.arima_model import ARIMA
import statsmodels.api as sm
import warnings
data = pd.read_csv("adidas quarterly sales.csv")
print(data)Time Period Revenue 0 2000Q1 1517 1 2000Q2 1248 2 2000Q3 1677 3 2000Q4 1393 4 2001Q1 1558 .. ... ... 83 2020Q4 5142 84 2021Q1 5268 85 2021Q2 5077 86 2021Q3 5752 87 2021Q4 5137 [88 rows x 2 columns]
The dataset contains two columns; Time Period and Revenue. The Time Period column contains the quarterly revenue of Adidas from 2000 to 2021, and the Revenue column contains the sales revenue in millions (euros). Let’s have a look at the quarterly sales revenue of Adidas:
import plotly.express as px
figure = px.line(data, x="Time Period",
y="Revenue",
title='Quarterly Sales Revenue of Adidas in Millions')
figure.show()
The sales revenue data of Adidas is seasonal as the quarterly revenue increases and decreases every quarter. Below is how we can check the seasonality of any time series data:
result = seasonal_decompose(data["Revenue"],
model='multiplicative', freq = 30)
fig = plt.figure()
fig = result.plot()
fig.set_size_inches(15, 10)
I will use the Seasonal ARIMA (SARIMA) model to forecast the quarterly sales revenue of Adidas. Before using the SARIMA model, it is necessary to find the p, d, and q values. You can learn how to find p, d, and q values from here.
As the data is not stationary, the value of d is 1. To find the values of p and q, we can use the autocorrelation and partial autocorrelation plots:
pd.plotting.autocorrelation_plot(data["Revenue"])
plot_pacf(data["Revenue"], lags = 20)
Now here’s how to train a SARIMA model to predict the quarterly revenue of Adidas:
model=sm.tsa.statespace.SARIMAX(data['Revenue'],
order=(p, d, q),
seasonal_order=(p, d, q, 12))
model=model.fit()
print(model.summary()) SARIMAX Results
==========================================================================================
Dep. Variable: Revenue No. Observations: 88
Model: SARIMAX(5, 1, 2)x(5, 1, 2, 12) Log Likelihood -548.520
Date: Mon, 05 Sep 2022 AIC 1127.041
Time: 07:45:33 BIC 1161.803
Sample: 0 HQIC 1140.921
- 88
Covariance Type: opg
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
ar.L1 -1.5796 0.391 -4.044 0.000 -2.345 -0.814
ar.L2 -1.4321 0.587 -2.438 0.015 -2.583 -0.281
ar.L3 -0.8305 0.626 -1.328 0.184 -2.057 0.396
ar.L4 -0.5179 0.821 -0.630 0.528 -2.128 1.092
ar.L5 -0.2655 0.491 -0.541 0.589 -1.228 0.697
ma.L1 1.5056 0.518 2.906 0.004 0.490 2.521
ma.L2 0.9697 0.623 1.557 0.120 -0.251 2.190
ar.S.L12 -1.1270 362.141 -0.003 0.998 -710.910 708.656
ar.S.L24 -1.3418 312.728 -0.004 0.997 -614.277 611.594
ar.S.L36 -0.7832 174.955 -0.004 0.996 -343.688 342.122
ar.S.L48 -0.1847 50.633 -0.004 0.997 -99.423 99.054
ar.S.L60 -0.0098 8.921 -0.001 0.999 -17.496 17.476
ma.S.L12 0.3046 362.082 0.001 0.999 -709.363 709.972
ma.S.L24 0.8602 221.641 0.004 0.997 -433.548 435.269
sigma2 1.909e+05 4.01e+05 0.476 0.634 -5.96e+05 9.78e+05
===================================================================================
Ljung-Box (L1) (Q): 0.00 Jarque-Bera (JB): 427.98
Prob(Q): 0.96 Prob(JB): 0.00
Heteroskedasticity (H): 7.35 Skew: -2.04
Prob(H) (two-sided): 0.00 Kurtosis: 13.97
===================================================================================
Now let’s forecast the quarterly revenue of Adidas for the next eight quarters:
predictions = model.predict(len(data), len(data)+7) print(predictions)
88 6078.793918 89 5186.311373 90 6293.196600 91 5751.905629 92 5911.946881 93 5499.784229 94 6389.627988 95 5728.806969 Name: predicted_mean, dtype: float64
Here’s how we can plot the predictions:
data["Revenue"].plot(legend=True,
label="Training Data",
figsize=(15, 10))
predictions.plot(legend=True, label="Predictions")
Summary
So this is how you can perform business forecasting using the Python programming language. In Business Forecasting, we aim to forecast future sales, expenditure, or revenue by using the historical Time Series data generated by the business. I hope you liked this article on Business Forecasting using Python. Feel free to ask valuable questions in the comments section below.
