- Forex prediction - Prediction using neural networks
- Time Series Prediction with LSTM Recurrent Neural Networks ...
- Financial time series prediction by using neural networks ...
- TIME SERIES PREDICTION WITH FEED-FORWARD NEURAL NETWORKS
- Time Series Prediction and Neural Networks
- Forecasting of Forex Time Series Data Based on Deep ...
- Time Series Prediction Using Recurrent Neural Networks ...

Hey guys, submitted by astronights to learnmachinelearning [link] [comments] I'm working on predicting forex prices and I have time series data for several currencies for the OHLC data. I'm aiming to try predictions using classical scikit-learn models, statsmodels(such as ARIMA) and even neural networks iteratively. I'm not sure how I should transform the data as the raw values do not appear to be stationary using the Dickey-Fueller test. Could you help? ARIMA Residual Results Green -> Forecast |

I have been reading research papers on financial time series prediction where the researchers use neural networks to forecast the closing price of some stock by using time series of various other securities as input features in the neural network.

For different types of securities, the financial time series can easily differ in length due to the number of trading days each security has. For example, crude oil (CL: WTI) as a commodity trades on a 5-day window (Monday-Friday), whereas FOREX trades on a 7-day window (Monday-Sunday).

Given that this unevenness in the trading days can lead to the input features being unequal in length and series having trading information for days other series cannot incorporate, how does one deal with such a problem, given that:

submitted by clutchking_asiimov to learnmachinelearning [link] [comments]
For different types of securities, the financial time series can easily differ in length due to the number of trading days each security has. For example, crude oil (CL: WTI) as a commodity trades on a 5-day window (Monday-Friday), whereas FOREX trades on a 7-day window (Monday-Sunday).

Given that this unevenness in the trading days can lead to the input features being unequal in length and series having trading information for days other series cannot incorporate, how does one deal with such a problem, given that:

- The simple feed-forward neural network takes in equal-sized input vectors
- Sequential models such as RNN/LSTM can take advantage of padding but at the trade-off of disrupting the original time-series

Hey guys, I'm trying to find some people who can help me out with a project that involves trying to predict bitcoin and forex markets (and potentially other time series data sets in the future) using artificial neural networks. Originally the site was exclusively bitcoin, but I've been making some pretty significant changes recently. Here is what I've done so far:

http://www.btcpredictions.com

Here, you can sign up for free and see charts displaying predicted prices for bitcoin and currency pairs (only 2 pairs so far but adding more soon). You can also see how each chart performed over the past 24 hours. The charts are created using a relatively simple neural network right now, but I'm looking to get into some more complicated types of neural networks and methods of training, which I'm happy to talk about in more detail if anyone is interested.

What I need most (which might be obvious) is help with front end development. That being said, I would love to work with back end engineers too and AI guys who want to help me develop some of the additional services I'm trying to create. One example of one of these services is that I would like to create a trade simulator where people can test out trading strategies without risking real money.

Feel free to contact me at [email protected] if you are interested, and we can set up a time to discuss how we can work together!

-Kevin

submitted by K128kevin to codeprojects [link] [comments]
http://www.btcpredictions.com

Here, you can sign up for free and see charts displaying predicted prices for bitcoin and currency pairs (only 2 pairs so far but adding more soon). You can also see how each chart performed over the past 24 hours. The charts are created using a relatively simple neural network right now, but I'm looking to get into some more complicated types of neural networks and methods of training, which I'm happy to talk about in more detail if anyone is interested.

What I need most (which might be obvious) is help with front end development. That being said, I would love to work with back end engineers too and AI guys who want to help me develop some of the additional services I'm trying to create. One example of one of these services is that I would like to create a trade simulator where people can test out trading strategies without risking real money.

Feel free to contact me at [email protected] if you are interested, and we can set up a time to discuss how we can work together!

-Kevin

Neural Network approaches to time series prediction are briefly discussed, and the need to find the appropriate sample rate and an appropriately sized input window identified. Relevant theoretical results from dynamic systems theory are briefly introduced, and heuristics for finding the appropriate sampling rate and embedding dimension, and thence window size, are discussed. The method is ... This paper proposes a C-RNN forecasting method for Forex time series data based on deep-Recurrent Neural Network (RNN) and deep Convolutional Neural Network (CNN), which can further improve the prediction accuracy of deep learning algorithm for the time series data of exchange rate. We fully exploit the spatio-temporal characteristics of forex time series data based on the data-driven method ... Okay so far we have only changed the topology but remember one major issue in time-series prediction is the sampling of the data. So let us try that now. Load the training set BSW210. Make a network just as before but this time its topology is 10-3-3-1 and train it. Don’t change the parameters, just set the max iterations to 1000. There are different neural network variants for particular tasks, for example, convolutional neural networks for image recognition and recurrent neural networks for time series analysis. Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. Furthermore, some research has compared deep learning with ... Time series prediction problems are a difficult type of predictive modeling problem. Unlike regression predictive modeling, time series also adds the complexity of a sequence dependence among the input variables. A powerful type of neural network designed to handle sequence dependence is called recurrent neural networks. The Long Short-Term Memory network or LSTM network is a type of recurrent ... Learn about sequence problems, long short-term neural networks and long short-term memory, time series prediction, test-train splits, and neural network models. Prediction using neural networks, Forex prediction Prediction using neural networks ... each of these time series have the following values: zero for interval below 0, close value in the interval 0-number of values, and again zero after the last known value. EURUSD - EUR USD forex currency pair data; USDJPY - EUR USD forex currency pair data; USDCHF - EUR USD forex currency pair data; EURJPY ...

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Experiment of the Armax algorithm applied to the time series. In addition to neural network algorithms, bio-inspired algorithms, this is one of the methods used by the company for prediction. Data Science for IoT Conference - London - 26th Jan 2017. Jakob Aungiers discussing the use of LSTM Neural Network architectures for time series prediction a... How to predict time-series data using a Recurrent Neural Network (GRU / LSTM) in TensorFlow and Keras. Demonstrated on weather-data. https://github.com/Hvass... Scott Crespo will cover the fundamentals of time series prediction and neural networks, and how to implement these sequence predictors using Python, TensorFlow, and Keras. The General Data Protection Regulation (GDPR), which came into effect on May 25, 2018, establishes strict guidelines for managing personal and sensitive data... In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in t... Learn the application of Time Series Neural Network using a simple data forecasting example with a MATLAB script. Learn concepts like "Open-loop network", "C...

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