Both classical forecasting methods and machine learning approaches are used to solve
forecasting problems. Deep artificial neural networks, one of the machine learning methods …

Long-term time series forecasting finds widespread applications in various domains such as
energy, finance, and transportation. Decomposing time series into sub-sequences with …

The prevalence of zero values in zero-inflated time-series (ZI-TS) data poses significant
challenges for traditional LSTM networks in learning long-term dependencies and trends …

Forecasting the electricity market, even in the short term, is a difficult task, due to the nature
of this commodity, the lack of storage capacity, and the multiplicity and volatility of factors that …

The wholesale price index (WPI) is a crucial economic indicator that provides insights into
the pricing dynamics of different goods within a country, especially potato commodities. In …

Multivariate long sequence time series forecasting is critical for various applications like
power consumption planning and weather forecasting. Effective forecasting in this context …

Timely detection of illnesses is vital to prevent severe infections and ensure effective
treatment, as it's always better to prevent diseases than to cure them. Sadly, many patients …

Solar energy forecasting is a very significant part of optimizing the solar power management
and integrating it into the energy grid. The accurate power output of the solar energy helps …

Accurately forecasting sales for fast-moving consumer goods (FMCG) remains a significant
challenge due to the volatile and multi-faceted nature of sales data. Existing methods often …

Accurate multi-energy load forecasting for distributed multi-energy systems is facing
challenges due to the complexity of multi-energy coupling and the inherent stochasticity. In …