Time series prediction plays a big role in economics.

The stock market courses, as well as the consumption of energy can be predicted to be able to make decisions. This tutorial shows one possible approach how neural networks can be used for this kind of prediction.

Stock market prediction - Wikipedia

It extends the Neuroph tutorial called " Time Series Prediction ", that gives a good theoretical base for prediction. To show how it works, we trained the network with the DAX German stock index data — for a month As a strategy we take the sequences from 4 days to predict each 5th day. In the training set 5th day is the supervised value. The data DAX can be downloaded from the following url one of the possibilities: TrainingSet Generator StockFileReader, StockSocketReader and TrainingData is available for download as a part of NetBeans project, however it is not integrated in the main program to simplify the source code.

Forex/Stock Day Trading Software with Neural Net Forecasting

Test dataset u sed: Each of the first 3 values in every record shows the date for DAX level. The last value in the records is DAX level. The next is the normalization of the training data in area The following formula offers it in two steps: Next, the network topology is defined: Actually, there is no rule for this, and usually it is determined experimentaly. However the common type of network used for prediction is a multi layer perceptron.

The output layer has only one node in this case. The good results were obtained with the following topology and paramet er set: At this point , we are ready to train and test the network.

For testing we'll use prepared data set in which the DAX data are given from the 27,28,29 and Since the network is initialised with random weight values, the test results will differ from a calculation to calculation. After five tests it came out with the following prediction - results for That is so called a committee - a collection of different neural networks, that together present the example.

It gives a much better result compared to other neural networks procedures. T he value which was official announced on that day is We are far from the usable result, although the calculations may look good with Neuroph allready. Good results were also obtained with Neuroph package in several other marketing predictions.

The next step in direction of obtaining better quantative results is changing the sequencie of calculations, which we carried out in previous example. We can use concurrent calculations to create the committee.

The committee tends not only to a stability but it also allows an effective relative control of training conditions. Relative scattering of the results from committee is the figure of merit in this case.

Deep Learning the Stock Market – Tal Perry – Medium

To create the concurrency we used the jetlang package. The next table was produced with 10 "members" of the committee.

The previous part is a very simplified introduction. We accepted that we will predict only "one step ahead To limit the task we need a theoretical stock market model. In this case the model is defined as following: This dynamic picture was simulated with CUDA. It shows only a fragment at the moment of this dynamics. The periodic signals prevail in this model. But which periodic signal is major? Where is the underfitting or overfitting of perceptron? Is it possible to automaticaly predict in this model?

We'll show an algorithm with autocorrection which demonstrates some basic development ideas. For simplification purporse, we'll take only a direction in the ocean: We have more than one sequence of calculations see "committee". The main circle gives the variation of number of points N in the window of time prediction. A head period is determined by N in every variation from " river waves ".

For each N will be variated the training sets: The middle value through committee is the result of this simple automatic flow.

How good is this model and the simple "river waves" prediction algorithm for your tasks, you should decide yourself. The simple DAX tests showed good results. The package is named "Stock Market RiverWaves" and the fourier analyser is included for control.

The package does not have committee feature, because the committee is allready submitted to StockMarketCommittee. These download packages are for versions of Neuroph older than 2. Neuroph framework with easyNeurons application 2. NetBeans project for Stock Market Prediction example 3. NetBeans project for Stock Market Committee example 4. NetBeans project for Stock Market RiverWaves example. Time Series Prediction Tutorial Chicken Prices Prediction Tutorial Multi Layer Perceptron Tutorial.

The network topology is 4 input neurons, 9 hidden neurons and 1 output neuron. In that case the number of the iterations was not be enough to achieve the accuracy. Valentin Steinhauer Short description Time series prediction plays a big role in economics. T o f ind the max value of DAX: We have carried out a simplification, have simply divided on