Published May 24, 2021

Forecasting Principles and Practice

Rob Hyndman delves into the evolving field of time series forecasting, discussing the impact of automated tools and deep learning, the complexities of COVID-19 prediction models, and the tools necessary for accurate forecasting in various domains, all while emphasizing the importance of adaptability and education in tackling new data challenges.

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Forecasting Principles and Practice

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Accessible Forecasting Resources

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Automated ForecastingRob Hyndman discusses the role of automated tools and deep learning in time series forecasting. He highlights the importance of evaluating forecast accuracy and the evolving impact of neural networks on large datasets.

Automated Forecasting

COVID-19 ForecastingRob Hyndman shares insights into the complex forecasting models used to predict COVID-19 cases and their impact on government policy. He highlights the importance of ensemble forecasting and the continuous adaptation required to address novel challenges.

COVID-19 Forecasting

Time Series EducationRob Hyndman, a Professor of Statistics at Monash University, discusses his textbook "Forecasting Principles and Practice." He highlights its accessibility and practical applications for students and practitioners in the field of data science.

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