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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 include 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.
Recurrent neural networks (RNNs), as opposed to feedforward neural networks, are designed for processing sequential data. Recently, RNNs have achieved a lot of success and are frequently used in text, speech, and video analysis but are less used for time series forecasting.
Join Yijing Chen, Dmitry Pechyoni, Angus Taylor, and Vanja Paunic to explore the basic concepts of building RNN models and learn how and when to apply them to time series forecasting. Yijing, Dmitry, Angus, and Vanja start with a quick overview of time series forecasting and traditional time series models before sharing a comprehensive introduction covering everything from feedforward neural networks to recurrent neural networks. You’ll then discover how RNN models are trained and dive into different RNN architectures, such as LSTM (long short-term memory) and GRU (gated recurrent unit).
Yijing, Dmitry, Angus, and Vanja conclude by examining how and when to use RNNs for time series forecasting. You’ll learn how to prepare time series data and how to use Keras to implement RNN models. Along the way, they share best practices for building state-of-the-art time series forecasting models using RNNs along with simple examples and successful applications of RNNs in Kaggle competitions.
Intro to time series forecasting
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.
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