Weighted time series forecasting¶

In many real-world scenarios, historical data is available for forecasting, but not all of it is reliable. For example, IoT sensors capture raw data from the physical world, but they are often prone to failure, malfunction, and attrition due to harsh deployment environments, leading to unusual or erroneous readings. Similarly, factories may shut down for maintenance, repair, or overhaul, resulting in gaps in the data. The Covid-19 pandemic has also affected population behavior, impacting many time series such as production, sales, and transportation.

The presence of unreliable or unrepresentative values in the data history poses a significant challenge, as it hinders model learning. However, most forecasting algorithms require complete time series data, making it impossible to remove these observations. An alternative solution is to reduce the weight of the affected observations during model training. This document demonstrates how skforecast makes it easy to implement this strategy with two examples.