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How can Python be used to forecast data trends?

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Forecasting data trends is an essential skill for operations research (OR), as it helps to optimize decisions, plan resources, and anticipate changes. Python is a powerful and versatile programming language that can be used to perform various forecasting methods, from simple to complex. In this article, we will explore some of the ways that Python can be used to forecast data trends, and how you can get started with some of the most popular libraries and tools.

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Ahmet Kandakoglu

Senior Manager, Data Science and Advanced Analytics @ IATA | Researcher | Lecturer | Top Operations Research Voice on…

1 Why Python?

Python has many advantages for forecasting data trends, such as its easy-to-learn syntax, a rich and diverse ecosystem of libraries and packages supporting various forecasting techniques, a strong community of developers and users, and the capability to integrate with other languages and platforms. With Python, you can use libraries like pandas, numpy, statsmodels, scikit-learn, and tensorflow to support your forecasting efforts. Additionally, the Python community offers support and guidance through forums and blogs, making it easier to get help when needed. Furthermore, Python can be integrated with other languages like R, SQL, Excel, or web applications to increase its functionality and compatibility.

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Ahmet Kandakoglu

Senior Manager, Data Science and Advanced Analytics @ IATA | Researcher | Lecturer | Top Operations Research Voice on LinkedIn
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I think you should not limit yourself to Python. R is very powerful with many libraries for statistical time series forecasting. I have been using R for my forecasting analytics course, focused on traditional methods as well as a limited focused on advanced methods. On the other hand, since machine learning methods have been increasingly adopted to solve forecasting problems, Python becomes a popular choice, especially in the industry with extensive library support.

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2 How to import and manipulate data?

Before you can forecast data trends, you need to import and manipulate your data to make it suitable for analysis. Python offers several options for importing data from different sources, such as CSV files, databases, APIs, or web scraping. Some of the most common libraries for importing data are pandas, requests, and BeautifulSoup.

Once you have imported your data, you need to manipulate it to make it ready for forecasting. This may involve cleaning, filtering, aggregating, transforming, or visualizing your data. Some of the most common libraries for manipulating data are pandas, numpy, matplotlib, and seaborn.

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3 How to choose a forecasting method?

Choosing a forecasting method is not a one-size-fits-all decision, as different methods have different assumptions, strengths, and limitations. The selection of a method depends on various factors, like the type and characteristics of the data, the purpose and scope of the forecast, the availability and quality of the data, and the complexity and flexibility of the method. Common forecasting methods include time series methods, such as exponential smoothing or ARIMA; regression methods like linear regression or polynomial regression; and machine learning methods like decision trees or neural networks. Each of these approaches utilizes historical data to model patterns and relationships in order to make predictions.

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Ahmet Kandakoglu

Senior Manager, Data Science and Advanced Analytics @ IATA | Researcher | Lecturer | Top Operations Research Voice on LinkedIn
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There are many factors affecting the choice of an appropriate forecasting method as mentioned in the article. Herein I would like to emphasize only one point. When there is a tie between any two methods, my recommendation is to use a causal/associative one (i.e. regression, causal machine learning methods). I have always observed that the decision makers/executives are interested in robust and explainable forecast as well as further actions based on the forecast. In other words, they need a better understanding of the underlying factors and their association to the forecast. At this point, a general strategy of choosing a causal method (if possible) will help you to support their decision and further actions.

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4 How to implement a forecasting method?

To implement a forecasting method in Python, you need to split your data into training and testing sets to prevent overfitting and evaluate your model performance. After selecting and fitting your model to the training data, using the relevant library and function for your method, you can tune the parameters to optimize the model fit. Then, make predictions on the testing data with the predict or forecast function of your model, generating the forecast values and intervals for your desired horizon. Lastly, evaluate your model accuracy using metrics such as mean absolute error, root mean squared error, mean absolute percentage error, or R-squared, comparing your predictions with the actual values.

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5 How to improve your forecast?

No forecast is perfect, and there is always room for improvement. To improve your forecast, you should explore and understand your data, experiment and compare different methods, and update and refine your model. Python offers a variety of options, libraries, and resources to perform various forecasting methods. As a beginner or an expert in OR, you can use Python to enhance your forecasting skills and outcomes. What forecasting methods have you used or want to learn in Python?

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can Python be used to forecast data trends?

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1 Why Python?

2 How to import and manipulate data?

3 How to choose a forecasting method?

4 How to implement a forecasting method?

5 How to improve your forecast?

6 Here’s what else to consider

Operations Research

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How can Python be used to forecast data trends?

1

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6

1 Why Python?

2 How to import and manipulate data?

3 How to choose a forecasting method?

4 How to implement a forecasting method?

5 How to improve your forecast?

6 Here’s what else to consider

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