4,327 research outputs found
Development of Neurofuzzy Architectures for Electricity Price Forecasting
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decisionâmaking process as well as strategic planning. In this study, a prototype asymmetricâbased neuroâfuzzy network (AGFINN) architecture has been implemented for shortâterm electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over wellâestablished learningâbased models
Regression Driven F--Transform and Application to Smoothing of Financial Time Series
In this paper we propose to extend the definition of fuzzy transform in order
to consider an interpolation of models that are richer than the standard fuzzy
transform. We focus on polynomial models, linear in particular, although the
approach can be easily applied to other classes of models. As an example of
application, we consider the smoothing of time series in finance. A comparison
with moving averages is performed using NIFTY 50 stock market index.
Experimental results show that a regression driven fuzzy transform (RDFT)
provides a smoothing approximation of time series, similar to moving average,
but with a smaller delay. This is an important feature for finance and other
application, where time plays a key role.Comment: IFSA-SCIS 2017, 5 pages, 6 figures, 1 tabl
Accounting for outliers and calendar effects in surrogate simulations of stock return sequences
Surrogate Data Analysis (SDA) is a statistical hypothesis testing framework
for the determination of weak chaos in time series dynamics. Existing SDA
procedures do not account properly for the rich structures observed in stock
return sequences, attributed to the presence of heteroscedasticity, seasonal
effects and outliers. In this paper we suggest a modification of the SDA
framework, based on the robust estimation of location and scale parameters of
mean-stationary time series and a probabilistic framework which deals with
outliers. A demonstration on the NASDAQ Composite index daily returns shows
that the proposed approach produces surrogates that faithfully reproduce the
structure of the original series while being manifestations of linear-random
dynamics.Comment: 21 pages, 7 figure
The History of the Quantitative Methods in Finance Conference Series. 1992-2007
This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.
The Effects of International F/X Markets on Domestic Currencies Using Wavelet Networks: Evidence from Emerging Markets
This paper proposes a powerful methodology wavelet networks to investigate the effects of international F/X markets on emerging markets currencies. We used EUR/USD parity as input indicator (international F/X markets) and three emerging markets currencies as Brazilian Real, Turkish Lira and Russian Ruble as output indicator (emerging markets currency). We test if the effects of international F/X markets change across different timescale. Using wavelet networks, we showed that the effects of international F/X markets increase with higher timescale. This evidence shows that the causality of international F/X markets on emerging markets should be tested based on 64-128 days effect. We also find that the effects of EUR/USD parity on Turkish Lira is higher on 17-32 days and 65-128 days scales and this evidence shows that Turkish lira is less stable compare to other emerging markets currencies as international F/X markets effects Turkish lira on shorten time scale.F/X Markets; Emerging markets; Wavelet networks; Wavelets; Neural networks
Forecasting Organic Food Prices: Testing and Evaluating Conditional Predictive Ability
Organic farmers, wholesalers, and retailers need reliable price forecasts to improve their decision- making practices. This paper presents a methodology and protocol to select the best-performing method from several time and frequency domain candidates. Weekly farmgate prices for organic fresh produce are used. Forecasting methods are evaluated on the basis of an aggregate accuracy measure and several out-of-sample predictive ability tests. Combining forecasts to improve on individual forecasts is investigated.Demand and Price Analysis,
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