onlyforreg,github

Designing a Machine Learning algorithm to predict stock prices is a subject of interest for economists and machine learning practitioners. Financial modelling is a challenging task, not only from an analytical perspective but also from a psychological perspective. After 2008 financial crisis, many financial companies and investors shifted their interest towards predicting future trends. Most of the existing methods for stock price forecasting are modelled using non-linear methods and evaluated on specific data sets. These models are not able to generalize for diverse datasets. Financial time series data is highly dynamic in nature and makes it difficult to analyze through statistical methods. Recurrent Neural Networks (RNN) based Long Short- Term Memory (LSTM) networks were able to capture the patterns of the sequences data meanwhile statistical methods tried to generalize by memorizing data instead of recognizing patterns. In this work, we examined the performance of LSTM model and statistical models over stock prices of different companies to generalize the model. The experimental results of this study show that, LSTM network outperformed traditional statistical methods like ARIMA, MA and AR models. Furthermore, we have noticed that, LSTM network was able to perform consistently on different data sets while statistical methods showed varied performance. Through this project, we addressed the gaps in current models of stock price prediction in both economic and machine learning perspective.

deep-learning-from-scratch

『ゼロから作る Deep Learning』(O'Reilly Japan, 2016)

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep-learning-with-tensorflow-book

深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.

deep-reinforcement-learning-book

書籍「つくりながら学ぶ！深層強化学習」のサポートリポジトリです

deep-time-series-prediction

Seq2Seq, Bert, Transformer, WaveNet for time series prediction.

deeplearningfortimeseriesforecasting

A tutorial demonstrating how to implement deep learning models for time series forecasting

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

dev-sidecar

开发者边车，github打不开，github加速，git clone加速，git release下载加速，stackoverflow加速

diebold-mariano-test

This is the codes for Diebold-Mariano test based on Python

diebold-mariano-test-1

This Python function dm_test implements the Diebold-Mariano Test (1995) to statistically test forecast accuracy equivalence for 2 sets of predictions with modification suggested by Harvey et. al (1997).

docs-l10n

Translations of TensorFlow documentation

ewokos

A microkernel os for ARM, well ported on Raspberry Pi(s)

examples

TensorFlow examples

fold

Deep learning with dynamic computation graphs in TensorFlow

forecasting

Time Series Forecasting Best Practices & Examples

forecasting-principles-and-practices

Implementing from Forecasting: Principles and Practices on Python

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