Time series data are sometimes affected by multiple cycles of different lengths. There can be a weekly cycle (better sales on Fridays), a monthly pattern (better sales at the beginning of the month as people have more cash after payday), and the effects of calendar seasonality (more tourists during summer, so better sales) might be present also. How to model multiple seasonality in one model? In this thesis, one could compare, for example, TBATS models (which allow multiple seasonalities) to alternative approaches
textabstractA recurring issue in modeling seasonal time series variables is the choice of the most a...
The behavior of daily series of economic activity like the consumption of electric energy, cash with...
textabstractThis book considers periodic time series models for seasonal data, characterized by para...
Time series may contain multiple seasonal cycles of different lengths. There are several notable fea...
This chapter reviews the principal methods used by researchers when forecasting seasonal time series...
A new approach to forecasting seasonal data is proposed where seasonal terms can be updated using th...
Multiple seasonalities play a key role in time series forecasting, especially for business time seri...
A new approach is proposed for forecasting a time series with multiple seasonal patterns. A state sp...
Within the multiplicative seasonal ARIMA modeling context, there are two forecasting models, ARIMA14...
New innovations state space modeling tools, incorporating Box-Cox transformations, Fourier series wi...
Within the multiplicative seasonal ARIMA modeling context, there are two forecasting models, RIMA14 ...
textabstractIn this paper we review recent developments in econometric modelling of economic time se...
State space modeling represents a statistical framework for exponential smoo- thing methods and it i...
Seasonality is one of the components in time series analysis and this seasonal component may occur m...
The general seasonal Complex Exponential Smoothing (CES) model is presented in this paper. CES is ba...
textabstractA recurring issue in modeling seasonal time series variables is the choice of the most a...
The behavior of daily series of economic activity like the consumption of electric energy, cash with...
textabstractThis book considers periodic time series models for seasonal data, characterized by para...
Time series may contain multiple seasonal cycles of different lengths. There are several notable fea...
This chapter reviews the principal methods used by researchers when forecasting seasonal time series...
A new approach to forecasting seasonal data is proposed where seasonal terms can be updated using th...
Multiple seasonalities play a key role in time series forecasting, especially for business time seri...
A new approach is proposed for forecasting a time series with multiple seasonal patterns. A state sp...
Within the multiplicative seasonal ARIMA modeling context, there are two forecasting models, ARIMA14...
New innovations state space modeling tools, incorporating Box-Cox transformations, Fourier series wi...
Within the multiplicative seasonal ARIMA modeling context, there are two forecasting models, RIMA14 ...
textabstractIn this paper we review recent developments in econometric modelling of economic time se...
State space modeling represents a statistical framework for exponential smoo- thing methods and it i...
Seasonality is one of the components in time series analysis and this seasonal component may occur m...
The general seasonal Complex Exponential Smoothing (CES) model is presented in this paper. CES is ba...
textabstractA recurring issue in modeling seasonal time series variables is the choice of the most a...
The behavior of daily series of economic activity like the consumption of electric energy, cash with...
textabstractThis book considers periodic time series models for seasonal data, characterized by para...