Welcome to this tutorial on time series forecasting using Python. Time series forecasting is a method of predicting future values based on historical data. Python provides several libraries that make it easy to perform time series forecasting.

Prerequisites

Before diving into the tutorial, make sure you have the following prerequisites:

  • Basic knowledge of Python programming
  • Familiarity with pandas and NumPy libraries
  • Access to Python environment (e.g., Jupyter Notebook, PyCharm, etc.)

Getting Started

To begin with, let's import the necessary libraries:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.tsa.arima_model import ARIMA

Data Preparation

For this tutorial, we will use the Air Passengers dataset, which is a monthly time series of the number of airline passengers. You can download the dataset from here.

# Load the dataset
data = pd.read_csv('AirPassengers.csv')

# Plot the data
plt.figure(figsize=(12, 6))
plt.plot(data)
plt.title('Airline Passengers')
plt.xlabel('Month')
plt.ylabel('Number of Passengers')
plt.show()

Airline Passengers

ARIMA Model

ARIMA stands for AutoRegressive Integrated Moving Average. It is a popular time series forecasting model. The ARIMA model has three parameters: p, d, and q.

  • p: The number of lag observations included in the model (lag order).
  • d: The number of times that the raw observations are differenced (degree of differencing).
  • q: The size of the moving average window (order of moving average).

To build an ARIMA model, we will use the ARIMA class from the statsmodels.tsa.arima_model module.

# Fit the ARIMA model
model = ARIMA(data, order=(5, 1, 0))
model_fit = model.fit(disp=0)

Forecasting

Once the model is trained, we can use it to forecast future values. Let's forecast the next 12 months:

# Forecast the next 12 months
forecast = model_fit.forecast(steps=12)[0]

# Plot the forecast
plt.figure(figsize=(12, 6))
plt.plot(data, label='Actual')
plt.plot(np.arange(len(data), len(data) + 12), forecast, label='Forecast')
plt.title('Airline Passengers Forecast')
plt.xlabel('Month')
plt.ylabel('Number of Passengers')
plt.legend()
plt.show()

Airline Passengers Forecast

Conclusion

In this tutorial, we learned how to perform time series forecasting using Python and the ARIMA model. By following the steps outlined in this tutorial, you can apply time series forecasting techniques to your own datasets.

For more information on time series forecasting, check out our advanced tutorial on time series analysis.