Earlier this year, my colleague Vishal Sharma gave a talk about time series forecasting best practices. The talk was well-received so we decided to turn it into a blog post. Below are some of the highlights from his talk. You can also follow the two software demos and try it yourself using our H2O AI Cloud .
(Note : The video links with timestamps may not work correctly with your browser. If so, right-click on the links and open them in a new window/tab.)
Vishal first discussed some use cases in different domains and explained what makes time series forecasting a special case of machine learning.
A typical time series pipeline is shown below. Vishal focused on preprocessing and forecasting components of the pipeline in this talk.
Some time series forecasting methods require data preprocessing. Here are some common techniques:
Vishal also discussed some of the commonly used time series models like AutoRegressive Integrated Moving Average (ARIMA) and Exponential Smoothing.
Here is one of the key takeaways – time series forecasting challenges and practical considerations:
Vishal then explained the inner workings of time series forecasting with our platform in great detail.
The first demo was about forecasting the number of passengers. You can find the data from Transportation Security Administration (TSA) here .
In short, time series models require data analysis, preprocessing, and hyperparameters optimization. Automatic machine learning (AutoML ) is a practical choice for time series forecasting as it can handle multiple constraints. For some use cases, it is useful to incorporate COVID data in model updates for better predictive power.
Time series AutoML with our AI Cloud platform can: