Time series forecasting is one of the most important topics in data science. Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. Examples of time series forecasting use cases are: financial forecasting, product sales forecasting, web traffic forecasting, energy demand forecasting for buildings and data centers and many more. However, most existing forecasting solutions use traditional time series and machine learning models. For complex forecasting problems, data scientists need to know how to leverage advanced techniques to generate more accurate forecasts.
Deep neural networks have achieved a lot of success for many applications. In particular, Recurrent neural networks (RNNs) are frequently used in text, speech and video analysis, being designed for processing sequential data. Additionally, Convolutional neural networks (CNNs) have achieved state-of-the-art performance on many computer vision tasks. These methods have only recently been applied to the task of time series forecasting but typically deliver strong performance. In this tutorial, we describe the basic concepts for building such models and show how and when to apply them to time series forecasting.
The tutorial will start with a quick overview of time series forecasting and neural network to provide the audience with a clear background on the kind of problems that we aim to solve.
Then we will give a comprehensive introduction starting from convolutional neural networks (CNNs) to recurrent neural networks (RNNs) such as LSTM and GRU. Each introduction will end with a hands-on exercise to help the audience solid the knowledge foundation before we move to advanced topics.
In the second part of the tutorial we will advance to how to apply Encoder-decoder RNN architecture to time series forecasting, followed by how to build state of art hybrid model with both CNNs and RNNs. Then we will show audience how to do hyper-perimeter tuning for such advanced model in real application.
Below is a summary of topics that we will cover in this tutorial:
· Tutorial Introduction
o Tutorial goals
o Tutorial agenda
o Target audience
o Why neural networks model for time series forecasting
· Knowledge Recap
o Time series recap
○ Time Series & Time Series Forecasting
○ Why is Time Series Forecasting important?
○ Questions to ask before building forecast model
o Feedforward Neural Network recap
○ Perceptron & multilayer perceptron
· Introduction to convolutional neural networks (CNNs)
o Introduction to CNNs
o How CNNs are trained
o Hands-on exercise: How to apply CNNs to time series forecasting
· Introduction to recurrent neural networks (RNNs)
○ What are RNNs?
○ How RNNs are trained: Backpropagation through time (BPTT)
○ Vanilla RNN and its gradient problems
○ Other RNN units
o RNN stacking
o Hands-on exercise: How to apply RNNs to time series forecasting
· Encoder-decoder RNN model
o What is encoder-decoder RNN architecture
o How to apply encode-decode RNN to multi-step time series forecasting
· Build state of art models for time series forecasting
o Hybrid CNN + RNN
o Hybrid traditional time series + RNN
o Hybrid RNN + feedforward
o Hands-on exercise
· Hyper-parameter tuning
o How to do hyper-parameter tuning
o Hands-on exercise
· Conclusion & Key takeaways
Yijing Chen is a senior data scientist in the Cloud AI Group at Microsoft, where she works with external customers in areas such as energy demand forecast, user mobile behavioral analysis, retail demand forecast, energy theft detection, product pricing, and medical claim denial prediction as well as on other projects using various machine learning methods. Yijing holds an MA in statistics from Harvard University.
Dmitry Pechyoni is a senior data scientist in the Cloud AI Group at Microsoft, where he works on building end-to-end data science solutions in various domains, including retail, energy management, and predictive maintenance. Previously, he built machine learning models for display advertising Akamai and MediaMath. Dmitry holds a PhD in theoretical machine learning from the Technion – Israel Institute of Technology.
Angus Taylor is a data scientist in the Cloud AI Group at Microsoft, where he builds data science solutions for external customers in the retail, energy, engineering, and package distribution sectors. He holds an MSc in AI from the University of Edinburgh.
Vanja Paunic is a data scientist in the Algorithms and Data Science Group at Microsoft London. She works on building machine learning solutions with external companies utilizing Microsoft’s AI Cloud Platform. She holds a PhD in computer science with a focus on data mining in the biomedical domain from the University of Minnesota.
Henry Zeng is a principal program manager in the Cloud AI Group at Microsoft, where he works with engineering team, partners and customers to ensure the success of ML platform. He has been in AI and data area for more than 10 years from database, NoSQL, Hadoop ecosystem, machine learning to deep learning. Prior to this role, he was the lead AI solution architect in Microsoft China working with partners and customer to land AI solutions in manufactory, retail, education and public service etc with Microsoft AI offerings. Henry holds a MS in computer science from Wuhan University.
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