Financial Time Series Forecasting Using Directed-Weighted Chunking SVMs

Financial Time Series Forecasting Using Artificial Neural Networks

Financial and capital markets (especially stock markets) are considered high return investment fields, which in the same time are dominated by uncertainty and volatility. Stock market prediction tries to reduce this uncertainty and consequently the risk. As stock markets are influenced by many economical, political and even psychological factors, it is very difficult to forecast the movement of...

Forecasting Financial Time-Series using Artificial Market Models

We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction in particular, forecasting financial time-series by training a third-party or ‘black box’ game on the financial data itself – was discussed by Johnson et al. in [10] and [13] and was based on som...

Financial Time Series Forecasting Using K - Nearest NeighborsClassi cation

Deriving a relationship that allows to predict future values of a time series is a challenging task when the underlying law is highly non linear. Usually, when facing with a problem of non-linear prediction, we are provided with the past history of the time series and we want to extract from that set of data a mathematical function that relates a certain window of past values with the value T t...

Financial Time Series Forecasting Using a Hybrid Neural- Evolutive Approach

The design of models for time series prediction has found a solid foundation on statistics. Recently, artificial neural networks have been a good choice as approximators to model and forecast time series. Designing a neural network that provides a good approximation is an optimization problem. Given the many parameters to choose from in the design of a neural network, the search space in this d...

Nonlinear Time Series in Financial Forecasting