Python Tutorial: How to Forecast Intermittent Time Series | Written by Marco Peixeiro | August 2023

Python Tutorial: How to Forecast Intermittent Time Series | Written by Marco Peixeiro | August 2023

Introduction:

Welcome to our complete guide on intermittent time series forecasting in Python! In this article, we will explore various techniques and methods for forecasting intermittent time series data. Intermittent time series, also known as sparse time series, consist of sporadic non-zero values scattered throughout time, while the rest of the values are zero. This type of time series presents a unique challenge for traditional forecasting models, making it necessary to use specialized methods tailored for sparse data. Throughout this guide, we will discuss different approaches to forecasting intermittent time series and provide Python implementations for each method. Stay tuned for practical examples and code snippets to help you better understand and apply these techniques. The full source code can be found on our GitHub repository. Let’s dive in and start mastering intermittent time series forecasting!

Full Article: Python Tutorial: How to Forecast Intermittent Time Series | Written by Marco Peixeiro | August 2023

A Complete Guide to Intermittent Time Series Forecasting in Python

Intermittent time series, also known as sparse time series, refer to data where non-zero values appear sporadically in time while the rest of the values are 0. This type of time series presents a unique challenge in forecasting, as traditional models are not well-equipped to handle intermittency. In this article, we will explore various methods for forecasting intermittent time series and provide a step-by-step guide using Python.

Understanding Intermittent Time Series

To better grasp the concept, let’s examine a couple of real-life examples. One common example of intermittent time series is rainfall over time. There can be consecutive days without rain, and when it does rain, the volume varies. Another example is seen in the demand for slow-moving or high-value items, such as spare parts in aerospace or heavy machinery.

Croston’s Method: A Baseline Model

Croston’s method is a widely used approach to forecasting sparse time series and often serves as a baseline model for evaluating more complex methods. It involves constructing two new series from the original data:

1. A series containing the time periods with only zero values
2. A series containing the time periods with non-zero values

To illustrate this, let’s consider a toy example. Suppose we have the following sparse time series:

[0, 5, 0, 0, 0, 0, 0, 10, 0, 0, 0, 15, 0, 0]

According to Croston’s method, we create two new series:

1. Non-zero values series: [5, 10, 15]
2. Time periods series: [1, 7, 11]

Implementing Croston’s Method in Python

Now, let’s move on to implementing Croston’s method using Python. Below is a step-by-step guide:

Step 1: Import the necessary libraries

Before we begin, make sure you have the required libraries installed. Import the necessary libraries, such as pandas, numpy, and matplotlib, using the following code:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

Step 2: Load and preprocess the data

Next, load the data into a pandas DataFrame and preprocess it as needed. Ensure the data is in the correct format for analysis.

Step 3: Split the data into non-zero and time periods series

Using Croston’s method, split the original series into two new series: one containing the non-zero values and another containing the time periods separating the non-zero values.

Step 4: Calculate the forecasted values

Using the non-zero values series, calculate the forecasted values based on Croston’s method. This typically involves estimating the average inter-arrival time and the average demand value.

Step 5: Evaluate and visualize the results

Once the forecasted values are obtained, evaluate the performance of the model using appropriate metrics, such as Mean Absolute Percentage Error (MAPE). Visualize the results to gain insights and make any necessary adjustments.

Conclusion

Intermittent time series forecasting presents unique challenges due to the sporadic nature of non-zero values. Croston’s method serves as a baseline model for forecasting sparse time series and can be implemented in Python. By understanding and applying these techniques, you can better forecast and analyze intermittent time series data for various applications.

Remember, practice makes perfect! Try implementing Croston’s method on your own data and explore other forecasting techniques tailored for intermittent time series. Good luck with your forecasting endeavors!

Summary: Python Tutorial: How to Forecast Intermittent Time Series | Written by Marco Peixeiro | August 2023

This article is a comprehensive guide on forecasting intermittent time series in Python, including a capstone project. Intermittent time series, which have sporadic non-zero values, can pose a challenge in forecasting. Traditional models struggle with this type of data, so alternative methods tailored for sparse time series are needed. The article explores various techniques for forecasting intermittent time series, discussing each model theoretically and providing Python implementations. The full source code is available on GitHub. Croston’s method, a common baseline model for forecasting sparse time series, is discussed in detail. The article also includes a toy example to illustrate the application of Croston’s method.

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