Time Series Forecastability Measures
This work addresses the problem for practitioners in forecasting domains like supply chain management by enabling pre-model assessment of forecastability, though it is incremental as it builds on existing metrics.
The paper tackled the problem of quantifying time series forecastability before model development by proposing two metrics, the spectral predictability score and largest Lyapunov exponent, and demonstrated their effectiveness on synthetic and real-world data, showing strong correlation with actual forecast performance.
This paper proposes using two metrics to quantify the forecastability of time series prior to model development: the spectral predictability score and the largest Lyapunov exponent. Unlike traditional model evaluation metrics, these measures assess the inherent forecastability characteristics of the data before any forecast attempts. The spectral predictability score evaluates the strength and regularity of frequency components in the time series, whereas the Lyapunov exponents quantify the chaos and stability of the system generating the data. We evaluated the effectiveness of these metrics on both synthetic and real-world time series from the M5 forecast competition dataset. Our results demonstrate that these two metrics can correctly reflect the inherent forecastability of a time series and have a strong correlation with the actual forecast performance of various models. By understanding the inherent forecastability of time series before model training, practitioners can focus their planning efforts on products and supply chain levels that are more forecastable, while setting appropriate expectations or seeking alternative strategies for products with limited forecastability.