TensorFlow Intermediate: Sequences and Time Series Forecasting

Name: TensorFlow Intermediate: Sequences and Time Series Forecasting
Author: Josh Turner

Master time series forecasting using TensorFlow

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Course: On-Demand

Hello, my name is Josh Turner, and these lessons you'll learn about time series and sequences. We'll learn how to pre-process a time series dataset, and finally we'll build our very own time series model. In this lesson, I'm going to teach you about the definitions of sequences and time series forecasting. Go ahead and open the Python editor of your choice, and create your own virtual environment. I'm using a Jupyter notebook, but feel free to use a Python file. The first thing we might want to discuss are the definition of sequence. What is a sequence exactly? A sequence, by definition, is simply an ordered list of some kind of data. Formally, a sequence is a collection of data points or events that are ordered in a specific manner. Okay, so that could be ordered by time. It could be ordered by, you know, who won the race, first, second, third. It can be ordered in many different ways, and you can choose many different orders. Okay, so why do we care about sequences? Well we care about sequences because we want to study the topic of time series. So let's discuss what exactly is time series, and what does it mean to make a time series forecast? So in particular, we want to discuss time series forecasting. The formal definition says that time series forecasting is a specific type of sequence analysis that focuses on predicting future values based on past observation. In time series forecasting, we're ordering our data by time. The data points are collected over regular intervals of time, and these can be anything from daily time intervals, monthly or yearly time intervals. And the goal is to predict or understand the data and the patterns that show up the data in the data and use that to predict future values of the data. Okay, so that was a mouthful, but you can see that we need to understand sequences which are these ordered lists of data, and then once we understand sequences, we can model them by using this technique of time series forecasting. We just have to understand that the sequences that we are modeling are ordered by time. Okay, so the next question would be, why do we actually care about this? Well, let's discuss some of the use cases for this. So some use cases for time series forecasting and sequences would be stock market prices, weather patterns, text data and the most famous sequence that lots of people know about DNA sequences. Stay tuned for the next lesson when I'll teach you more about sequences. Thanks for watching.

Intermediate Provider : Josh Turner 5 Lessons · 29m in English

Course Description

In this course, you will explore the world of time series forecasting using TensorFlow, a powerful open-source library for deep learning. Discover the techniques and tools to preprocess and prepare time series data, build sequential models, and make accurate predictions. From understanding the importance of data preprocessing to implementing state-of-the-art models, this course equips you with the skills to excel in the field of time series forecasting.

The course begins with a strong foundation in data preprocessing and preparation. You will learn how to handle missing values, normalize data, and handle categorical variables specific to time series data. With hands-on exercises and real-world examples, you will gain expertise in cleaning and transforming time series datasets.

We also explore the power of sequential models in time series forecasting, and dive into the concepts of recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs) for analyzing and predicting sequential patterns. We’ll discuss building and training these models using TensorFlow and its vast array of tools and functionalities.

Throughout the course, you will have the opportunity to apply your knowledge through hands-on coding exercises and projects. Working with real-world time series datasets helps you develop practical skills that can be directly applied to your own forecasting projects.

What You'll Learn

Preprocess and prepare time series data, including handling missing values, normalizing data, and handling categorical variables
Build sequential models for time series forecasting using TensorFlow
Train and optimize sequential models for accurate time series predictions
Apply advanced techniques such as hyperparameter tuning and ensembling
Evaluate the performance of time series models using appropriate metrics
Understand the fundamentals of time series forecasting and its applications

Key Takeaways

TensorFlow is a powerful open-source library for deep learning used here to build and train models for time series forecasting.
Data preprocessing and preparation form the foundation of the course, covering cleaning and transforming time series datasets.
Recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs) are used for analyzing and predicting sequential patterns.
The course includes hands-on coding exercises and projects using real-world time series datasets to develop practical, directly applicable skills.
Model performance is evaluated using appropriate metrics, alongside techniques such as hyperparameter tuning and ensembling.

Frequently Asked Questions

What will I learn in this course?

You will learn to preprocess and prepare time series data, build sequential models, train and optimize them for accurate predictions, apply techniques such as hyperparameter tuning and ensembling, and evaluate model performance using appropriate metrics, all using TensorFlow.

Which deep learning models does this course cover?

The course dives into recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs) for analyzing and predicting sequential patterns, and covers building and training these models using TensorFlow.

Is this course hands-on?

Yes. Throughout the course you apply your knowledge through hands-on coding exercises and projects, working with real-world time series datasets to develop practical skills you can apply to your own forecasting projects.

What topics are covered in the lessons?

The lessons cover an overview of sequences and time series forecasting, an introduction to sequences, preprocessing and data preparation for time series data, building a sequential model for time series forecasting, and training and evaluating the model.

What skills will I gain from this course?

You will build skills in forecasting, data science, financial forecasting, TensorFlow, and time series, aligned with the SAS Certified Specialist: Forecasting and Optimization area.