Add to ORKGThe non-linear structure of river flow time series can be adequately explained by regime switching models, however good fitting results do not guarantee an equal good forecasting performance. Aim of this paper is to evaluating an comparing different approaches to compute forecasts from regime switching models with respect to their predictive accuracy. To this purpose, different nonlinear prediction techniques are applied to a time series of hydrological data and their forecasting ability in predicting the future values of the observed series at different lead times are assess by means of a wide range of loss functions
In the recent past the Nonlinear Prediction (NLP) method, initially developed in the context of non...
In hydrology the ability to model the average daily river flow for rivers plays an important role in...
For more than a century, the study of streamflow recession has been dominated by seemingly physicall...
The non-linear structure of river flow time series can be adequately explained by regime switching m...
The paper presents a data-driven approach to the modelling and forecasting of hydrological systems b...
The performance of the self-exciting threshold autoregressive moving average model in forecasting ri...
To perform hydrological forecasting, time series methods are often employed. In univariate time seri...
The forecasting of monthly seasonal streamflow time series is an important issue for countries where...
In non-structural measurement of flood control, hydrologic forecasting plays a very important role. ...
Two nonlinear models, nonlinear prediction (NLP) and artificial neural networks (ANN), are compared ...
In this study, a nonparametric technique to set up a river stage forecasting model based on empirica...
Four methods of forecasting: „no-change", LOESS, local linear regression and Holt-Winters were appli...
Abstract: The focus of this paper is using nonparametric transfer function models in forecasting. No...
In this paper we investigate the forecast performance of nonlinear error‐correction models with regi...
Time series forecasting is the use of a model to forecast future events based on known past events....
In the recent past the Nonlinear Prediction (NLP) method, initially developed in the context of non...
In hydrology the ability to model the average daily river flow for rivers plays an important role in...
For more than a century, the study of streamflow recession has been dominated by seemingly physicall...
The non-linear structure of river flow time series can be adequately explained by regime switching m...
The paper presents a data-driven approach to the modelling and forecasting of hydrological systems b...
The performance of the self-exciting threshold autoregressive moving average model in forecasting ri...
To perform hydrological forecasting, time series methods are often employed. In univariate time seri...
The forecasting of monthly seasonal streamflow time series is an important issue for countries where...
In non-structural measurement of flood control, hydrologic forecasting plays a very important role. ...
Two nonlinear models, nonlinear prediction (NLP) and artificial neural networks (ANN), are compared ...
In this study, a nonparametric technique to set up a river stage forecasting model based on empirica...
Four methods of forecasting: „no-change", LOESS, local linear regression and Holt-Winters were appli...
Abstract: The focus of this paper is using nonparametric transfer function models in forecasting. No...
In this paper we investigate the forecast performance of nonlinear error‐correction models with regi...
Time series forecasting is the use of a model to forecast future events based on known past events....
In the recent past the Nonlinear Prediction (NLP) method, initially developed in the context of non...
In hydrology the ability to model the average daily river flow for rivers plays an important role in...
For more than a century, the study of streamflow recession has been dominated by seemingly physicall...