Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets

• URL: http://arxiv.org/abs/2204.06540v1
• Date: Wed, 13 Apr 2022 17:37:32 GMT
• Title: Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets
• Authors: Georgia Papacharalampous, Hristos Tyralis
• Abstract summary: We focused on 28 features that included (partial) autocorrelation, entropy, temporal variation, seasonality, trend, lumpiness, stability, nonlinearity, linearity, spikiness, curvature and others. We estimated these features for daily temperature, precipitation and streamflow time series from 511 catchments, and then merged them within regionalization contexts with traditional topographic, land cover, soil and geologic attributes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and their interplay. In this work, we deviated from this traditional path by formulating and extensively investigating the first regression-based streamflow regionalization frameworks that largely emerge from general-purpose time series features for data science and, more precisely, from a large variety of such features. We focused on 28 features that included (partial) autocorrelation, entropy, temporal variation, seasonality, trend, lumpiness, stability, nonlinearity, linearity, spikiness, curvature and others. We estimated these features for daily temperature, precipitation and streamflow time series from 511 catchments, and then merged them within regionalization contexts with traditional topographic, land cover, soil and geologic attributes. Precipitation and temperature features (e.g., the spectral entropy, seasonality strength and lag-1 autocorrelation of the precipitation time series, and the stability and trend strength of the temperature time series) were found to be useful predictors of many streamflow features. The same applies to traditional attributes, such as the catchment mean elevation. Relationships between predictor and dependent variables were also revealed, while the spectral entropy, the seasonality strength and several autocorrelation features of the streamflow time series were found to be more regionalizable than others.

Related papers

• FAITH: Frequency-domain Attention In Two Horizons for Time Series Forecasting [13.253624747448935]
  Time Series Forecasting plays a crucial role in various fields such as industrial equipment maintenance, meteorology, energy consumption, traffic flow and financial investment. Current deep learning-based predictive models often exhibit a significant deviation between their forecasting outcomes and the ground truth. We propose a novel model Frequency-domain Attention In Two Horizons, which decomposes time series into trend and seasonal components.
  arXiv  Detail & Related papers  (2024-05-22T02:37:02Z)
• Adapting to Length Shift: FlexiLength Network for Trajectory Prediction [53.637837706712794]
  Trajectory prediction plays an important role in various applications, including autonomous driving, robotics, and scene understanding. Existing approaches mainly focus on developing compact neural networks to increase prediction precision on public datasets, typically employing a standardized input duration. We introduce a general and effective framework, the FlexiLength Network (FLN), to enhance the robustness of existing trajectory prediction against varying observation periods.
  arXiv  Detail & Related papers  (2024-03-31T17:18:57Z)
• TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables [82.07393844821522]
  We propose a novel framework, TimeXer, to utilize external information to enhance the forecasting of endogenous variables. TimeXer significantly improves time series forecasting with endogenous variables and achieves consistent state-of-the-art performance in twelve real-world forecasting benchmarks.
  arXiv  Detail & Related papers  (2024-02-29T11:54:35Z)
• PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations [49.80959046861793]
  We present PDETime, a novel LMTF model inspired by the principles of Neural PDE solvers. Our experimentation across seven diversetemporal real-world LMTF datasets reveals that PDETime adapts effectively to the intrinsic nature of the data.
  arXiv  Detail & Related papers  (2024-02-25T17:39:44Z)
• Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting [10.892801642895904]
  We propose Distance-weighted Auto-regularized Neural network (DAN), a novel extreme-adaptive model for long-range forecasting of stremflow enhanced by polar representation learning. On four real-life hydrologic streamflow datasets, we demonstrate that DAN significantly outperforms both state-of-the-art hydrologic time series prediction methods and general methods designed for long-term time series prediction.
  arXiv  Detail & Related papers  (2023-12-14T09:16:01Z)
• Rethinking Urban Mobility Prediction: A Super-Multivariate Time Series Forecasting Approach [71.67506068703314]
  Long-term urban mobility predictions play a crucial role in the effective management of urban facilities and services. Traditionally, urban mobility data has been structured as videos, treating longitude and latitude as fundamental pixels. In our research, we introduce a fresh perspective on urban mobility prediction. Instead of oversimplifying urban mobility data as traditional video data, we regard it as a complex time series.
  arXiv  Detail & Related papers  (2023-12-04T07:39:05Z)
• Hydroclimatic time series features at multiple time scales [0.0]
  A comprehensive understanding of the behaviours of the various geophysical processes requires detailed investigations across temporal scales. We propose a new time series feature compilation for advancing and enriching such investigations in a hydroclimatic context. This specific compilation can facilitate largely interpretable feature investigations and comparisons in terms of temporal dependence, temporal variation, "forecastability", lumpiness, stability, nonlinearity (and linearity), trends, spikiness, curvature and seasonality.
  arXiv  Detail & Related papers  (2021-12-02T17:43:30Z)
• Instance-wise Graph-based Framework for Multivariate Time Series Forecasting [69.38716332931986]
  We propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps. The key idea of our framework is aggregating information from the historical time series of different variables to the current time series that we need to forecast.
  arXiv  Detail & Related papers  (2021-09-14T07:38:35Z)
• Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale [0.0]
  We study the relationships between descriptive time series features and actual time series forecastability. We apply this framework to three global datasets. We find that this forecastability in terms of Nash-Sutcliffe efficiency is strongly related to several descriptive features.
  arXiv  Detail & Related papers  (2021-07-25T19:15:19Z)
• Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series [77.47313102926017]
  Radflow is a novel model for networks of time series that influence each other. It embodies three key ideas: a recurrent neural network to obtain node embeddings that depend on time, the aggregation of the flow of influence from neighboring nodes with multi-head attention, and the multi-layer decomposition of time series. We show that Radflow can learn different trends and seasonal patterns, that it is robust to missing nodes and edges, and that correlated temporal patterns among network neighbors reflect influence strength.
  arXiv  Detail & Related papers  (2021-02-15T00:57:28Z)
• Latent State Inference in a Spatiotemporal Generative Model [3.7525506486107267]
  We focus on temperature and weather processes, including wave propagation dynamics, for which we assume that universal causes apply throughout space and time. A recently introduced DIsed Stemporal graph graph artificial Neural Architecture (DISTANA) is used and enhanced to learn such processes. We show that DISTANA, when combined with a retrospective latent state inference principle called active tuning, can reliably derive location-respective hidden causal factors.
  arXiv  Detail & Related papers  (2020-09-21T12:59:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.