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An evaluation of time series forecasting models on water consumption data: A case study of Greece

arXiv 2303.17617 ↗First posted 30 Mar 2023cs.LGcited by 0

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Abstract (arXiv)

In recent years, the increased urbanization and industrialization has led to a rising water demand and resources, thus increasing the gap between demand and supply. Proper water distribution and forecasting of water consumption are key factors in mitigating the imbalance of supply and demand by improving operations, planning and management of water resources. To this end, in this paper, several well-known forecasting algorithms are evaluated over time series, water consumption data from Greece, a country with diverse socio-economic and urbanization issues. The forecasting algorithms are evaluated on a real-world dataset provided by the Water Supply and Sewerage Company of Greece revealing key insights about each algorithm and its use.

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References · 4

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2017Deep and Confident Prediction for Time Series at Ubercited by 50
  3. 2019Deep Learning for Time Series Forecasting: The Electric Load Casecited by 31
  4. 2019AR-Net: A simple Auto-Regressive Neural Network for time-seriescited by 20

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