Etsformer: Exponential smoothing transformers for time-series forecasting
Transformers have been actively studied for time-series forecasting in recent years. While
often showing promising results in various scenarios, traditional Transformers are not …
often showing promising results in various scenarios, traditional Transformers are not …
Mega: moving average equipped gated attention
The design choices in the Transformer attention mechanism, including weak inductive bias
and quadratic computational complexity, have limited its application for modeling long …
and quadratic computational complexity, have limited its application for modeling long …
Reinforcement learning framework for freight demand forecasting to support operational planning decisions
Freight forecasting is essential for managing, planning operating and optimizing the use of
resources. Multiple market factors contribute to the highly variable nature of freight flows …
resources. Multiple market factors contribute to the highly variable nature of freight flows …
Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability
Delivering useful hydrological forecasts is critical for urban and agricultural water
management, hydropower generation, flood protection and management, drought mitigation …
management, hydropower generation, flood protection and management, drought mitigation …
Forecasts of sustainable consumption in small economies
R Kontautienė, T Stravinskas… - Journal of international …, 2024 - ceeol.com
Sustainable consumption is becoming an increasingly important aspect of our consumer
society. The scarcity of natural resources is a growing concern in many countries …
society. The scarcity of natural resources is a growing concern in many countries …
Time Series Forecasting with Statistical, Machine Learning, and Deep Learning Methods: Past, Present, and Future
E Spiliotis - Forecasting with Artificial Intelligence: Theory and …, 2023 - Springer
Time series forecasting covers a wide range of methods extending from exponential
smoothing and ARIMA models to sophisticated machine learning ones, such as neural …
smoothing and ARIMA models to sophisticated machine learning ones, such as neural …
[HTML][HTML] Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale
Statistical analyses and descriptive characterizations are sometimes assumed to be offering
information on time series forecastability. Despite the scientific interest suggested by such …
information on time series forecastability. Despite the scientific interest suggested by such …
[HTML][HTML] Local and global trend Bayesian exponential smoothing models
This paper describes a family of seasonal and non-seasonal time series models that can be
viewed as generalisations of additive and multiplicative exponential smoothing models to …
viewed as generalisations of additive and multiplicative exponential smoothing models to …
Spatiotemporal forecasting of water change trends in Urmia Lake through to 2030, using STC-based models
H Ahmadi, BS Mousavi, M Argany… - Hydrological …, 2024 - Taylor & Francis
The purpose of this paper is to forecast the spatiotemporal water change trends in Urmia
Lake through 2030. Three space–time cube-based models were applied. The forest-based …
Lake through 2030. Three space–time cube-based models were applied. The forest-based …
Assessment of the Black Sea Grain Initiative: Crisis Management via Maritime Transportation
G Ekleme, F Yercan - Transport Policy, 2025 - Elsevier
After the conflict between Russia and Ukraine, an agreement regarding the export of grains
produced in these countries has been signed. Consequently, the export of grains from Black …
produced in these countries has been signed. Consequently, the export of grains from Black …