Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm

A Novel Weighted Ensemble Technique for Time Series Forecasting

Improvement of time series forecasting accuracy is an active research area having significant importance in many practical domains. Extensive works in literature suggest that substantial enhancement in accuracies can be achieved by combining forecasts from different models. However, forecasts combination is a difficult as well as a challenging task due to various reasons and often simple linear...

Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting

Operational flood forecasting requires that accurate estimates of the uncertainty associated with modelgenerated streamflow forecasts be provided along with the probable flow levels. This paper demonstrates a stochastic ensemble implementation of the Sacramento model used routinely by the National Weather Service for deterministic streamflow forecasting. The approach, the simultaneous optimizat...

A Locally Weighted Learning Tutorial using Vizier

Imitation-based Learning of Bipedal Walking Using Locally Weighted Learning

Walking is an extremely challenging problem due to its dynamically unstable nature. It is further complicated by the high dimensional continuous state and action spaces. We use locally weighted projection regression (LWPR) as a locally structurally adaptive nonlinear function approximator as the basis for learned control policies. Empirical evidence suggests that control policies for high dimen...

Locally Weighted Learning

Locally Weighted Learning is a class of function approximation techniques, where a prediction is done by using an approximated local model around the current point of interest. This paper gives an general overview on the topic and shows two different solution algorithms. Finally some successful applications of LWL in the field of Robot Learning are presented.