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. You may need to use time series forecasting for financial forecasting, product sales forecasting, web traffic forecasting, energy demand forecasting for buildings and data centers, or many other reasons. However, most existing forecasting solutions use traditional time series and machine learning models. For your complex forecasting problems, you 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, RNNs are frequently used in text, speech, and video analysis, being designed for processing sequential data. Additionally, 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.
Yijing Chen, Dmitry Pechyoni, Angus Taylor, Vanja Paunic, and Henry Zeng explore the basic concepts for building such models and show you how and when to apply them to time series forecasting. Once you have a quick overview of time series forecasting and neural networks and a clear background on the problem, Yijing, Dmitry, Angus, Vanja, and Henry dive deep into CNNs and RNNs, including long short-term memory (LSTM) and GRU. At the end of each introduction, you’ll solidify the basics with a hands-on exercise, then move into more advanced topics. You’ll learn how to apply encoder-decoder RNN architecture to time series forecasting, build state-of-the-art hybrid models with CNNs and RNNs, and do hyperparameter tuning for such advanced models in real application.
Introduction to CNNs
Introduction to RNNs
Encoder-decoder RNN model
Build state-of-the-art models for time series forecasting
Conclusion and 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 on the AI platform team at Microsoft, where he works with the engineering team, partners, and customers to ensure AzureML is the best ML platform in the cloud. He’s been in the AI and data area for more than 14 years in areas such as database, big data, machine learning, and deep learning. Previously, he was the lead AI solution architect at Microsoft China, where he worked with partners and customers to land AI solutions in manufactory, retail, finance, education, and public service. Henry holds an MS in computer science from Wuhan University.
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