Mastering the underlying characteristics of carbon price changes can help governments formulate correct policies to keep efficient operation of carbon markets, and investors take effective measures to evade their investment risks. Empirical mode decomposition (EMD), a self-adaption data analysis approach for nonlinear and non-stationary time series, can accurately explain the formation mechanis...

today, the discussion of regularity monetary policy in achieving inflation and output gap stabilization is important. in this paper, we investigated the persistence of the output gap in iran economy and then by using a hybrid new keynesian model based on quarterly data for 1990:1- 2011:3. we compared three alternative instrumental rules in monetary policy for iran economy. the results indicated...

We outline initial concepts for an immune inspired algorithm to evaluate and predict oil price time series data. The proposed solution evolves a short term pool of trackers dynamically, with each member attempting to map trends and anticipate future price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. The resulting sequence ...

Online auctions have become increasingly popular in recent years, and as a consequence there is a growing body of empirical research on this topic. Most of that research treats data from online auctions as crosssectional, and consequently ignores the changing dynamics that occur during an auction. In this article we take a different look at online auctions and propose to study an auction’s pric...

In this paper, the data analysis and short term price forecasting in Iran electricity market as a market with pay-as-bid payment mechanism has been considered. The proposed method is a modified singular spectral analysis (SSA) method. SSA decomposes a time series into its principal components i.e. its trend and oscillation components, which are then used for time series forecasting effectively....

Abstract: Accurate forecasting of carbon price is important and fundamental for anticipating the changing trends of the energy market, and, thus, to provide a valid reference for establishing power industry policy. However, carbon price forecasting is complicated owing to the nonlinear and non-stationary characteristics of carbon prices. In this paper, a combined forecasting model based on vari...

The goal of this work is to derive models for forecasting the final price of ongoing online auctions. This forecasting task is important not only to the participants of an auction who compete against each other for the lowest price, but also to designers of bidder-side agents. Forecasting prices in online auctions is challenging from a statistical pointof-view because traditional forecasting mo...

Electricity price forecasting is becoming more important in everyday business of power utilities. Good forecasting models can increase effectiveness of producers and buyers playing roles in electricity market. Price is also a very important element in investment planning process. This paper presents a forecasting technique to model day-ahead spot price using well known ARIMA model to analyze an...