This paper explores the implications of asset return predictability on longterm portfolio choice when return forecasting variables exhibit long memory. We model long memory using the class of fractionally integrated time series models. Important predictor variables for U.S. data, like the dividend-price ratio and nominal and real interest rates, are non-stationary with orders of integration aro...

Electricity imbalance pricing provides the ultimate incentive for generators and suppliers to contract with one another ahead of time deliver against their obligations. As delivery approaches, traders must judge whether trade-out a position or settle it in balancing market at as-yet-unknown price. Forecasting price (and related volumes) is therefore necessity short-term markets. However, this t...

This thesis reports the discovery of spectral patterns in ECG signals that exhibit a temporal behavior correlated with an approaching Ventricular Tachyarrhythmic (VTA) event. A computer experiment is performed where a supervised learning algorithm models the ECG signals with the targeted behavior, applies the models on other signals, and analyzes consistencies in the results. The procedure was ...

Iron ore future has an extreme influence on global financial markets. Thus, we are supposed to consider the day-of-the-week effect, which is important volatility of iron price, although less existing literature considers it. The effect added basic heterogeneous autoregressive (HAR) model so that a new type established. empirical results show higher accuracy in forecasting price and contains muc...

• short-term inclement weather forecasting (for hurricanes, etc.), • contaminant plume forecasting in both urban environments (for coordinating emergency response) and battlefield environments (for coordinating troop movements), • long-term ocean current forecasting (for El Niño, climate change, etc.), and • MHD/plasma forecasting (for sunspot cycles, over terms of years, in order to plan space...

Load forecasting is an important problem in the operation and planning of electrical power generation. To minimize the operating cost, electric supplier will use forecasted load to control the number of running generator unit. Short-term load forecasting (STLF) is for hour to hour forecasting and important to daily maintaining of power plant. Most important factors in load forecasting includes ...

This study proposes using a random forest model for short-term electricity load forecasting. This is an ensemble learning method that generates many regression trees (CART) and aggregates their results. The model operates on patterns of the time series seasonal cycles which simplifies the forecasting problem especially when a time series exhibits nonstationarity, heteroscedasticity, trend and m...