Forecasting specialization

Welcome to our Forecasting Specialization! Through this course bundle, you have the opportunity to enroll in all our specialized forecasting courses in one go and at a discounted price.

By enrolling in this comprehensive bundle, you'll gain access to a curated collection of courses specifically designed to equip you with the skills needed to navigate the intricacies of business forecasting.

Whether you're new to the field or looking to enhance your existing knowledge, our program provides a tailored learning experience that caters to diverse levels of expertise.

Enroll now and embark on a path that promises not just knowledge, but practical and applicable skills to elevate your proficiency in forecasting.

What is forecasting?

Forecasting is the process of making predictions or estimates about future events based on historical data and analysis of trends. In various fields such as business, economics, weather, and finance, forecasting plays a crucial role in decision-making and planning. The aim is to anticipate future outcomes or trends by utilizing statistical models, data analysis, and relevant information.

In business, for example, forecasting helps organizations predict sales, make demand planning, and organize the supply chain, enabling them to make informed decisions on resource allocation and strategy.

Overall, forecasting provides a valuable tool for individuals and businesses alike to navigate uncertainties and make more informed choices in the face of an ever-changing future.

Forecasting examples

Forecasting is applied across various industries to make informed decisions and plan for the future.

Here are some real-world examples of forecasting:

  • Sales Forecasting:
  • Businesses use historical sales data and market trends to predict future sales volumes. This helps in inventory management, production planning, and resource allocation.
  • Financial Forecasting:
  • Financial analysts use time series forecasting to predict future financial performance, including revenue, expenses, and profitability. This aids in budgeting and financial planning.
  • Stock Price Forecasting:
  • Investors and financial analysts use historical stock data and market trends to predict future stock prices. This information guides investment decisions.
  • Energy Consumption Forecasting:
  • Utility companies use forecasting methods to predict future energy demand. This assists in planning energy production, optimizing resources, and ensuring a stable power supply.
  • Travel Demand Forecasting:
  • Transportation authorities use forecasting models to predict future travel demand for infrastructure planning, such as building new roads or expanding public transportation.

These examples highlight the versatility and importance of forecasting in diverse fields, where it serves as a valuable tool for informed decision-making and strategic planning.

How do we forecast future values?

Forecasting employs various methodologies to anticipate future events or trends. Time series analysis is a common approach, involving the examination of historical data points collected over time to identify patterns and trends.

Forecasting with statistical models

Statistical time series models, such as exponential smoothing and ARIMA (AutoRegressive Integrated Moving Average), are frequently used in time series analysis for forecasting. These models leverage past time series data points to make predictions about future values, assuming that historical patterns will continue.

Forecasting with machine learning

In recent years, forecasting with machine learning models, like linear regression, random forests and gradient boosting trees, has gain a lot of popularity. Machine learning algorithms are more versatile than statistical forecasting models, in that they can predict non-linear relationships, combine the use of time series data with exogenous variables, and also forecast multiple time series at a time.

To forecast with traditional machine learning models, we need to first be able to extract features from our time series. We can do so by using lags and windows, and creating features that capture seasonality and trend.

Forecasting with deep learning

Additionally, we can also use neural networks for time series forecasting. These artificial intelligence models are well suited to take time series data as input, and return time series as output, while recognizing intricate patterns in data, making them particularly useful for complex and non-linear relationships.

In summary, forecasting utilizes time series analysis, statistical models like ARIMA, traditional machine learning models like xgboost, and neural networks to analyze historical data and make predictions about future events or trends. Each method has its strengths and is chosen based on the nature of the data and the complexity of the forecasting task at hand. All in all, these resources allow us to improve forecast accuracy and make sound decisions.

Course 1: Forecasting with Machine Learning

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Course 2: Feature Engineering for Forecasting

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Why take our forecasting courses?

Enrolling in our forecasting specialization is offers a unique opportunity for learners like yourself, who will get a 360 view of the field of time series forecasting through our specialized courses.

Each course focuses on one aspect of forecasting, be it, for example, forecasting with traditional machine learning models, forecasting with statistical models, or how to extract features from your time series to make sound analyses and predictions about the future. And because each course is specialized in that one topic, they will take you from complete beginner to forecasting pro.

In our courses, we teach the fundamentals of forecasting and also advanced methods and strategies, together with best practices that are used in the industry. We provide plenty of hands-on Python code that’ll show you how to implement each method, and which you can reuse in your own projects.

The course materials are curated to provide you with a comprehensive understanding of forecasting techniques, making it an invaluable resource for anyone aiming to bolster their employability in today's data-driven landscape. With a focus on in-demand skills, this interactive program ensures that you not only grasp theoretical concepts but also gain practical experience through real-world applications.

Don’t settle for shorter and superficial courses that will only scratch the surface of the topic. With our specialization you will get the knowledge, skills and tools you need to start forecasting in your own projects in no time.

You will learn theory, practice and also understand which are the state-of-the-art Python libraries that you can use TODAY to streamline your forecasting pipelines.

Course materials

Each of our forecasting courses are packed with more than 10 hours of video tutorials, where we explain the various forecasting and feature engineering methods and show you how to implement them in Python. You will also find quizzes and assignments to practice and test the skills you just learned.

Who is this course for?

This course is tailored for individuals with a background in data science, computer science, or predictive analytics who want to learn more about time series forecasting. Whether you're a data enthusiast looking to deepen your understanding of forecasting techniques or a computer science professional aiming to expand your skill set, this program is designed to cater to your aspirations.

The course structure is curated to accommodate both beginners and those with a foundational background in these fields, providing a comprehensive exploration of the fundamentals of forecasting combined with practical implementations and advice on how to create and deploy forecasting models in the real—world.

If you are new to forecasting, this specialization will equip you with everything you need to decide which forecasting models you should use, and how to implement them in Python. If you already know the fundamentals of forecasting, this specialization will provide different perspectives into forecasting by using different methods than those you are familiar with, for example, forecasting with traditional machine learning models. And it will also provide you with an arsenal of strategies that you can leverage to extract meaningful features from your time series.

Throughout the specialization you will also discover various open-source Python libraries that will help you make your forecasting pipelines smoother, faster and free from data leakage.

Course prerequisites

The specialization aims to take your from zero, to forecasting hero. Having said this, familiarity with basic machine learning concepts and Python programming will help you get the most out of our forecasting courses.

Knowledge about basic regression models, such us linear regression and decision trees, and basic model evaluation metrics like R-squared and the mean squared error, will empower you to focus on the new concepts more quickly.

Wrapping up

This comprehensive forecasting specialization leaves no stone unturned. Through our specialized forecasting courses, you’ll learn how to forecast with traditional machine learning models, how to extract features from time series, and how to forecast with statistical models.

The curriculum features real-world case studies with practical demonstrations, accompanied by hands-on Python code examples in Jupyter notebooks. These examples serve as valuable references, aids for practice, and resources for reuse in individual projects.

With a 30-day money-back guarantee, enrolling in this specialization entails no financial risk. Seize the opportunity today. Enroll now to participate in the world's most comprehensive forecasting course.

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Your instructors

Kishan Manani, PhD

Kishan Manani, PhD

Kishan is a machine learning and data science lead, course instructor, and open source software contributor. He has contributed to well known Python packages including Statsmodels, Feature-engine, and Prophet. He presents at data science conferences including ODSC and PyData. Kishan attained a PhD in Physics from Imperial College London in applied large scale time-series analysis and modelling of cardiac arrhythmias; during this time he taught and supervised undergraduates and master's students.

Soledad Galli, PhD

Sole is a lead data scientist, instructor and developer of open source software. She created and maintains the Python library Feature-engine, which allows us to impute data, encode categorical variables, transform, create and select features. Sole is also the author of the "Python Feature engineering Cookbook" by Packt editorial.

Soledad Galli, PhD
English subtitles

English subtitles

Instructor support

Instructor support

Certificate of completion

Certificate of completion

Frequently Asked Questions

Do I pay once or monthly?

Up to you. You can pay once and get lifetime access, or you can subscribe an pay monthly, in which case you'll be able to access the course so long your subscription is active.

What if I don't like the course?

If you pay once to get lifetime access, there is a 30-day money back guarantee. If you don't find the course useful, contact us within the first 30 days of purchase and you will get a full refund.

If you decide to subscribe, there is a 3-day free trial, during which you can watch the content and decide if it is right for you. If not, just cancel the subscription from your user panel.

Will I get a certificate?

Yes, you'll get a certificate of completion after completing all lectures, quizzes and assignments from a course.

You'll get one completion certificate per course.

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