Time Series Forecasting with Python

What Exactly is Time Series Forecasting?

A time series involves examining data points collected at successive intervals, such as days, weeks, years, or even specific business days. Accounting for time is essential for effective data analysis, and time series techniques help you leverage this aspect to gain insights.

You may already know about forecasting. Think of your daily weather forecast—it's all about attempting to anticipate the future as accurately as possible.

In essence, time series forecasting is about applying time series data and techniques to estimate what the future is likely to hold.

Pretty handy, right?

Similar to a weather forecaster, although perfection in predicting the future is elusive, using the right data, tools, and analytical methods can significantly enhance the reliability of your forecasts.

Why Choose Project-Based Learning?

Working on a project is one of the most effective methods to acquire knowledge. At ZTM, we emphasize learning through practice. This hands-on approach prepares you for real-world challenges.

Moreover, to secure your ideal job, you'll need to showcase portfolio projects that demonstrate your expertise to potential employers. This particular project will make a fantastic addition to your portfolio, impressing future employers and helping you achieve your career aspirations.

What Skills Will This Project Equip Me With?

Through this project, you'll gain insights into the four key forecasting models currently favored by Business Data Analysts, along with the latest tools needed for implementation.

Additionally, you'll explore:

1. Facebook Prophet: a reliable forecasting tool from Meta (Facebook) that aids in planning and goal-setting for businesses.

2. SARIMAX Modeling in Python: What’s SARIMAX? It stands for "Seasonal Auto-Regressive Integrated Moving Average with eXogenous" factors—quite a mouthful! Simply put, SARIMAX is an excellent resource for time series analysis, part of the ARIMA family of models.

3. LinkedIn Silverkite: Another robust forecasting tool from LinkedIn, Silverkite! Many argue it's even superior to Facebook Prophet, as it employs automated modeling to enhance forecast precision.

4. Recurrent Neural Networks: Explore and apply advanced neural networks for time series forecasting, including Long Short-Term Memory, a highly sought-after deep learning forecasting model!

5. Ensemble Methodology: a popular approach that merges predictions from various models to boost accuracy.

One of the best aspects of this project (and all ZTM courses) is that you won’t be navigating this journey alone.

By signing up today, you’ll gain access to our exclusive live online community classroom, allowing you to learn alongside thousands of peers, alumni, mentors, TAs, and instructors.

Ready to master time series forecasting and enhance your predictive skills? Click Start Learning Now to join the Academy and begin working on this captivating portfolio project. We can't wait to see you inside!