The success of autonomous systems will depend upon their ability to safely navigate human-centric environments. This motivates the need for a real-time, probabilistic forecasting algorithm for pedestrians, cyclists, and other agents since these predictions will form a necessary step in assessing the risk of any action. This paper presents a novel approach to probabilistic forecasting for pedest...

We build a prediction model for a set of Emerging Markets and the US market and we evaluate its ability to forecast equity risk premia. Our models include traditional global and local variables as the dividend yield or a credit spread and we include some variables that has been rarely been used as predictor of emerging equity returns: the implied volatility of the US market. We study the period...

A Hidden Markov Model (HMM) is used to classify an out of sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. Instead of maximizing a likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimati...

In this paper, we propose a benchmarking of supervised machine learning techniques (neural networks, Gaussian processes and support vector machines) in order to forecast the Global Horizontal solar Irradiance (GHI). We also include in this benchmark a simple linear autoregressive (AR) model as well as two naive models based on persistence of the GHI and persistence of the clear sky index (denot...

A new approach is proposed for forecasting a time series with multiple seasonal patterns. A state space model is developed for the series using the single source of error approach which enables us to develop explicit models for both additive and multiplicative seasonality. Parameter estimates may be obtained using methods adapted from general exponential smoothing, although the Kalman filter ma...

This study applies an approximate dynamic factor model to forecast three macroeconomic variables of Taiwan – inflation based on consumer price index, unemployment rate, and industrial production growth rate. Our data contain 95 macroeconomic variables of Taiwan and 89 international time series during 1981Q1-2006Q4. We perform out-of-sample forecasting from a rolling-window estimation scheme and...

Many risk management strategies, including hedging the price risk using forward or futures contracts require accurate forecasts of basis, i.e., spot price minus the futures price. Recent literature in this area has applied nonlinear time-series models, which are refinements of the linear autoregressive models that allow the parameters to transition from one regime to another. These parametric n...

We examine the ability of short rates and yield spreads to forecast the growth in Australian industrial output. We find that since 1990, the short rate has a significant increase in its predictive power for forecasting output growth in many industries. We document this increase. The yield spread, on the other hand, is useful in predicting the growth of industries with a `longer' production cycl...