With large penetration of wind power into grids, the accurate prediction generation is becoming extremely important. Planning, scheduling, maintenance, trading and smooth operations all depend on accuracy prediction. However due to highly non-stationary chaotic behaviour wind, forecasting for different intervals time becomes more challenging. Forecasting over spans essential applications energy...

Road traffic prediction is a critical component in modern smart transportation systems. It provides the basis for traffic management agencies to generate proactive traffic operation strategies for alleviating congestion. Existing work on near-term traffic prediction (forecasting horizons in the range of 5 minutes to 1 hour) relies on the past and current traffic conditions. However, once the fo...

In this paper we propose a strategy for forecasting the term structure of interest rates which may produce significant gains in predictive accuracy. The key idea is to use the restrictions implied by Affine Term Structure Models (ATSM) on a vector autoregression (VAR) as prior information rather than imposing them dogmatically. This allows to account for possible model misspecification. We appl...

Introducing a five factor more flexible model this paper verifies the in-sample fitting and the out-of-sample forecasting performance of several extensions of the Nelson and Siegel (1987) parametric model which was reinterpreted by Diebold and Li (2006). We used different rules for fixing the parameters λ that govern the models ́ exponential components shapes, and predictions were made for diffe...

We take up the challenge of forecasting out-of-sample monthly returns on stock market indices. Recent contributions show that a wide range of popular predictors poorly forecast the US equity premium when the performance is compared to the average predictor. We revisit these findings focusing on three aspects. First, we report results for four major stock markets: US, UK, Germany and Japan. We f...

We use high-frequency intra-day realized volatility to evaluate the relative forecasting performance of several models for the volatility of crude oil daily spot returns. Our objective is to evaluate the predictive ability of time-invariant and Markov switching GARCH models over different horizons. Using Carasco, Hu and Ploberger (2014) test for regime switching in the mean and variance of the ...

The predictive accuracy of various econometric models, including random walks, vector autoregressive and vector error-correction models, are investigated using daily futures prices of 4 commodities (the S&P500 index, treasury bonds, gold and crude oil). All models are estimated using a rolling window approach, and evaluated by both in-sample and out-of-sample performance measures. The criteria ...

This paper evaluates the effectiveness of selected volatility models in forecasting Value-at-Risk (VaR) for 1-day and 10-day horizons. The latter is the actual reporting horizon required by the Basel Committee on Banking Supervision, but not considered in existing studies. The autoregressive stochastic volatility (Taylor, 1982) is found to be less effective than simpler ARCH type models such as...

This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning of demand-side Bulk Electricity Market Customers (BEMCs). The Ontario electricity market is selected as the primary case market and its structure is studied in detail. A set of explanatory variable cand...

Though it is in many ways the backbone of an entire company, Forecasting is still handled by many organizations without supporting data or automation whatsoever. Additionally, many companies do not monitor their own forecasting accuracy. Even those that do, often do not fully go beyond the aggregate data and break down forecast accuracy by specific regions, platforms, time horizons, products an...