# Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

arXiv: https://arxiv.org/abs/2108.00846 · HTML: https://mlsanity.com/paper/2108.00846/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 127 references that are in the map.
- First posted on arXiv 25 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [GRATIS: GeneRAting TIme Series with diverse and controllable characteristics](https://mlsanity.com/paper/1903.02787/) (2019)
- [Highly comparative time-series analysis: The empirical structure of time series and their methods](https://mlsanity.com/paper/1304.1209/) (2013)
- [Boosting algorithms in energy research: A systematic review](https://mlsanity.com/paper/2004.07049/) (2020)
- [Automatic time-series phenotyping using massive feature extraction](https://mlsanity.com/paper/1612.05296/) (2016)
- [Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability](https://mlsanity.com/paper/2001.00811/) (2020)
- [Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity](https://mlsanity.com/paper/2010.12833/) (2020)
- [Evaluating Catchment Models as Multiple Working Hypotheses: on the Role of Error Metrics, Parameter Sampling, Model Structure, and Data Information Content](https://mlsanity.com/paper/2009.00729/) (2020)

## Built on by (most cited first, 7 of 7)

- [A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting](https://mlsanity.com/paper/2206.08998/) (2022)
- [Comparison of machine learning algorithms for merging gridded satellite and earth-observed precipitation data](https://mlsanity.com/paper/2301.01252/) (2022)
- [Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets](https://mlsanity.com/paper/2204.06540/) (2022)
- [Forecastability as an Information-Theoretic Limit on Prediction](https://mlsanity.com/paper/2603.27074/) (2026)
- [FEG-Pro: Forecast-Error Growth Profiling for Finite-Horizon Instability Analysis of Nonlinear Time Series](https://mlsanity.com/paper/2605.17282/) (2026)
- [Hydroclimatic time series features at multiple time scales](https://mlsanity.com/paper/2112.01447/) (2021)
- [Features of the Earth's seasonal hydroclimate: Characterizations and comparisons across the Koppen-Geiger climates and across continents](https://mlsanity.com/paper/2204.06544/) (2022)

## Abstract (arXiv)

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest suggested by such assumptions, the relationships between descriptive time series features (e.g., temporal dependence, entropy, seasonality, trend and linearity features) and actual time series forecastability (quantified by issuing and assessing forecasts for the past) are scarcely studied and quantified in the literature. In this work, we aim to fill in this gap by investigating such relationships, and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns. To this end, we follow a systematic framework bringing together a variety of -- mostly new for hydrology -- concepts and methods, including 57 descriptive features and nine seasonal time series forecasting methods (i.e., one simple, five exponential smoothing, two state space and one automated autoregressive fractionally integrated moving average methods). We apply this framework to three global datasets originating from the larger Global Historical Climatology Network (GHCN) and Global Streamflow Indices and Metadata (GSIM) archives. As these datasets comprise over 13 000 monthly temperature, precipitation and river flow time series from several continents and hydroclimatic regimes, they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale...