Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting
نویسندگان
چکیده
منابع مشابه
Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting
In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones,...
متن کاملElectricity Load Forecasting by Combining Adaptive Neuro-fuzzy Inference System and Seasonal Auto-Regressive Integrated Moving Average
Nowadays, electricity load forecasting, as one of the most important areas, plays a crucial role in the economic process. What separates electricity from other commodities is the impossibility of storing it on a large scale and cost-effective construction of new power generation and distribution plants. Also, the existence of seasonality, nonlinear complexity, and ambiguity pattern in electrici...
متن کاملApplication of a New Hybrid Method for Day-Ahead Energy Price Forecasting in Iranian Electricity Market
Abstract- In a typical competitive electricity market, a large number of short-term and long-term contracts are set on basis of energy price by an Independent System Operator (ISO). Under such circumstances, accurate electricity price forecasting can play a significant role in improving the more reasonable bidding strategies adopted by the electricity market participants. So, they cannot only r...
متن کاملComparative Analysis of Short-Term Price Forecasting Models: Iran Electricity Market
As the electricity industry has changed and became more competitive, the electricity price forecasting has become more important. Investors need to estimate future prices in order to take proper strategy to maintain their market share and to maximize their profits. In the economic paradigm, this goal is pursued using econometric models. The validity of these models is judged by their forecastin...
متن کاملRobust nonlinear canonical correlation analysis: application to seasonal climate forecasting
Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pear...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Technometrics
سال: 2011
ISSN: 0040-1706,1537-2723
DOI: 10.1198/tech.2011.09050