Gately E. Ed. Sieci neuronowe. Prognozowanie finansowe i projektowanie systemów transakcyjnych. num. z ang. Warszawa WIG-PressGoogle Scholar. 7. PDF | Neural networks have properties known to be effective in the modeling of economic phenomena. The process of constructing neural models that represent . european ecs p4s5adx manual pdf ed gately sieci neuronowe pdf societies in the bronze age pdf. Download Bronze Age is a time period characterized.

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Skip to main content. Log In Sign Up. Neural network analysis of time series data. The process of constructing neural mode]s that represent one-dimensionaL time series is reviewed and demon, strated.

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Explicit attention is paid to the evaluation of the mode]s. AI1 steps required by theory and practice are demonstrated through an example. The The neural networks exhibit a set of jeuronowe, due to which nature of these time sequences is also changing – at present they can become a useful tool for modeling and prediction they often form long time series, consisting of high fre- of socioeconomic phenomena.

The propriety of their appli- quency data and exhibiting a complex, nonlinear structure. The possibilities of applying neural models in predicting time series are the subject matter of the following: In such a case the evaluation of the function b The process agtely construction of a neural model gateoy parameters requires less effort and leads to construc- of exploration of all the available data sets and then tion of a model, which is easier in interpretation, evaluation of a model able to describe the regulari- ties found in the data.

The most im- describing the formation of the studied dependencies, portant disadvantages of neural networks include: It can also Many gatelg can be indicated, which have been devoted provide the means for comprehensive analysis gattely the to applications of neural networks in the analysis of econ- studied part of reality.

Gatelu basic information about the omy data e. Such a way of realisation of the neural model of time series neural model calculation results in a considerable re- duction of time required for the execution of all the Several basic stages can be distinguished in the process of necessary calculations, construction of a neural model of time series: The application of neural models in not always justified. The analysis of one-dimensional time series found.

Preprocessing of original data and decomposition of teristics of the studied variables.

In the case of time time series will be discussed in point 4 of the paper. Point 5 series of economic origin, the process of making the demonstrates a construction method of models for particu- data operational usually consists of calculation of the gayely components. The aggregate e is presented in point series of relative or absolute increments, dynamic in- 6, whereas the evaluation methods can be found in point 7, dices, taking the logarithm or square root of the data, or extraction of information concerning the data sign 4 itself.

The preliminary data analysis and – preliminary determination of series characteristics decomposition of the time series – operation aimed at a preliminary test, checking whether any regularities can be found in the data. For The process of preliminary data analysis and decomposi- the case of finance time series analysis, the prelimi- tion of the time series includes the following operations: Primary methods at this stage of analysis are: BDS test [Lin, ].

For the error function. The necessity of such transformation follows from the speciflc features of the applied neural models the ne- The order of consecutive stages is not always consistent cessity to adapt the variability range of the processed with the sequence presented above, because some phases values to the range of values generated by the output of the process are often performed many times during the neurons.

The way in which the transformation is per- construction of a single model. As an example, one can formed in most cases is not related to the system gen- consider the network’s learning, which is often performed erating the data. The 6 Construction of the aggregate basic questions which have to be answered during the con- model struction of each model include: The data aggregation is usually taken into account include the application of feed-forward multi-layer networks multi-layer per- carried out in a way reverse to the process of the original ceptronsthe networks with radial basic functions, series decomposition usually by executing the summation or multiplication of the partial results.

The choice ofthe proper network type depends The theoretical values of the aggregate model are ob- mainly on the character of the described phenomenon tained by aggregation of the results calculated by the partial and the structure of data, models.

Due to that relation a currences. The construction of a properly working possibility emerges to estimate the model quality measures model requires the identifications of the previous val- independently for each set.

The results of the studies carried out by the authors confirm the usefulness in that field of 7 Evaluation of model’s correctness methods employing the genetic algorithms. The employed evaluation process should involve of neuron model.

Particularly promising results can many aspects of the problem. Therefore generating the data. The exact ues of errors will be different in a system predicting the value of the instrument is deflned as follows: These measures are based on the comparison of the actual value where with the theoretical neuronowr p1. The quality of neural model’s predic- profit resulting from the realization of investments made tions is usually compared to the predictions obtained from basing on the calculated predictions.

Model systems of in- aimed at checking whether: The authors have decided to present a neural model of time series describing The analysis aieci the series of remainders can be done vi- a monthly number of foreign airline passengers. The length nomic prognostic systems, because the estimated quality of the time series amounts to observations. For evaluation of a prog- maxi r1 nostic system the accuracy of the decisions taken with its The scaling method is justified because all values of dis- help should be evaluated in the first place.

The respec- cussed series are greater than zero. Calculations connected tive model evaluation system depends on the field of the with the transformation of original data have resulted in a model’s application. Financial prediction quality measllres determine the model’s utility in the process of finance decision making. Next the scaled series has been submitted to the decom- Table 1: J 5 2 I2 The following step in the calculation was the evalu- 4 2 2 1 ation of neural models indicating regularities occurring 5 2 1 in each of the seven components.

Taken into consideration of the number of scribing behaviour of each distinguished component. Therefore a set of and neurons. For this purpose 80 elements were drawn from the all set and – in model of the component 1 the values with indexes; added to a learning set. Re- maining elements were included in the testing set.

It has to – in model of the component 2 the values with indexes: The proposed model – in model of the component 5 the value with index: Next step was isused, the evaluation of parameters of the models describing be- haviour of distinguished components.

Learning was carried – in model of the component 7 the value with index: It has to be stressed that the method of selecting input The genetically minimised fitting function was dependent variables based on a genetic algorithm may be applied in- on the error value for the learning set describing ability to dependently of the neural network.

Each of them described the structure of one network data presented in table 2. The above method was repeated 7 times – sepa- Obtained results confirm the utiiity of the adopted rately for each component. G Object-oriented time series has been presented. Se- ods of modeling of time series. It is worth stressing that lected Papers. Having an effectively working model the predic- [14] Masters T. It is particularly useful in fore- casting future values of economic indexes.

Badania op- work Time Series Forecasting. Neural Computing and Applications,I. WIG – Press, Warszawa.

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