Robust Forecasting Methods: A Comparative Study

In this study, we evaluated various time series forecasting methods for large-scale production settings, using a dataset from the GluonTS library. We compared the performance of these methods in terms of mean quantile loss, which measures how well the predictions match the actual values. The results showed that our proposed method, DeepNPTS, consistently outperformed other state-of-the-art methods in most datasets, especially when dealing with non-Gaussian data or data with multiple zeros.
To understand why this matters, imagine you’re trying to predict how many cookies you’ll bake tomorrow based on past sales data. You could use a simple method like averaging the past numbers, but that might not give you an accurate forecast if the pattern of cookie sales changes over time. A more sophisticated method like DeepNPTS takes into account the patterns in the data and can make better predictions even when things are changing rapidly.
However, it’s important to note that these methods have limitations too. Some datasets might be too complex for these models to handle, and they may not work well in all situations without proper tuning. Think of it like trying to build a car with parts from different cars – while some parts might work well together, others might not fit properly and cause problems on the road.
In summary, our study showed that DeepNPTS is a strong performer among time series forecasting methods for large-scale production settings, particularly when dealing with non-Gaussian data or data with multiple zeros. While these models have limitations, they can provide valuable insights and help make better predictions in complex systems.