Time series forecasting is a crucial task in various fields of business and science. There are
two coexisting approaches to time series forecasting, which are statistical methods and …

Time series forecasting covers a wide range of methods extending from exponential
smoothing and ARIMA models to sophisticated machine learning ones, such as neural …

Time series forecasting is one of the most active research topics. Machine learning methods
have been increasingly adopted to solve these predictive tasks. However, in a recent work …

Time series forecasting is a research domain that has its origin in the field of statistics and
econometrics. Since there are many prediction problems involving a time component, the …

Although it is a difficult task, forecasting time-series is nevertheless an important task that
boasts numerous research efforts. Markidakis et al.[1] emphasise two facts about the field …

The research and development of time-series forecasting requires a relative assessment of
forecast accuracy, although determining which model or method to select is difficult. This …

Time Series Forecasting is vital for wide range of domains such as financial market
forecasting, earthquake forecasting, weather forecasting, electric power demand forecasting …

Time series data is ubiquitous, and accurate time series forecasting is vital for many real-
world application domains, including retail, healthcare, supply chain, climate science, e …

Time series forecasting is one of the most active research topics. Machine learning methods
have been increasingly adopted to solve these predictive tasks. However, in a recent work …

Time series forecasting is a research area with applications in various domains,
nevertheless without yielding a predominant method so far. We present ForeTiS, a …