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## Deep learning and applied math for financial forecasting [IV] – Dimensionality Reduction [Python]

In previous posts we proposed basic procedures necessary for performing one-step-ahead and multy-step-ahead prediction of S&P500 index based on prices of its constituents. In this post we will focus on accuracy and performance increase using dimension reduction...

read more## How to build custom Deep learning neural network from scratch in Python [II] – Testing

In previous blog post we proposed how custom Deep learning neural network can be built from scratch using Python. In this post, we will present some verification and testing results. Let us consider a very simple example of learning the shape of a sinusoidal...

read more## How to build custom Deep learning neural network from scratch in Python [I] – Basics

Feed-Forward Networks This post illustrates steps required to build and train feed-forward neural networks from scratch in Python. Implementing the Network in Code We will consider the following \( L \)-layer network structure \[ X^{(l)}_i=...

read more## Deep learning and applied math for financial forecasting [III] – Deep learning with TensorFlow [Python]

This post illustrates basic procedure necessary for performing one-step-ahead prediction of S&P500 index based on prices of its constituents using feed-forward neural networks implemented in TensorFlow using Keras API. TensorFlow is open source library for dataflow...

read more## Deep learning and applied math for financial forecasting [II] – simple linear regression, n-steps-ahead [Python]

Let us now attempt to predict future values of S&P500 based on the current values of the constituents. The results obtained for the prediction horizont of n=10 steps ahead will be elaborated, but the same procedure cn be applied for arbitrary n . The results will, off...

read more## Deep learning and applied math for financial forecasting [I] – simple linear regression [Python]

Introduction Financial forecasting, especially stock price forecasting is one of the key components of the everyday market activities. Small increase of accuracy can lead to significant profit so such algorithms and models attracts significant visibility in academic...

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