Predicting a fast and accurate model for stock price forecasting is been a
challenging task and this is an active area of research where it is yet to be
found which is the best way to forecast the stock price. Machine learning, deep
learning and statistical analysis techniques are used here to get the accurate
result so the investors can see the future trend and maximize the return of
investment in stock trading. This paper will review many deep learning
algorithms for stock price forecasting. We use a record of s&p 500 index data
for training and testing. The survey motive is to check various deep learning
and statistical model techniques for stock price forecasting that are Moving
Averages, ARIMA which are statistical techniques and LSTM, RNN, CNN, and FULL
CNN which are deep learning models. It will discuss various models, including
the Auto regression integration moving average model, the Recurrent neural
network model, the long short-term model which is the type of RNN used for long
dependency for data, the convolutional neural network model, and the full
convolutional neural network model, in terms of error calculation or percentage
of accuracy that how much it is accurate which measures by the function like
Root mean square error, mean absolute error, mean squared error. The model can
be used to predict the stock price by checking the low MAE value as lower the
MAE value the difference between the predicting and the actual value will be
less and this model will predict the price more accurately than other models.

使用机器学习、深度学习和统计分析技术来预测股票价格的准确模型一直是个具有挑战性的任务，本文将回顾多种深度学习算法，包括移动平均、ARIMA、LSTM、RNN、CNN 和全卷积神经网络模型，以及通过误差计算方法如均方根误差、平均绝对误差、均方误差等来评估其准确性，结果表明，较低的平均绝对误差（MAE）值可以更准确地预测股价。

利用深度学习和统计模型进行股票预测研究

A Study on Stock Forecasting Using Deep Learning and Statistical Models

High-frequency trading requires fast data processing without information lags
for precise stock price forecasting. This high-paced stock price forecasting is
usually based on vectors that need to be treated as sequential and
time-independent signals due to the time irregularities that are inherent in
high-frequency trading. A well-documented and tested method that considers
these time-irregularities is a type of recurrent neural network, named long
short-term memory neural network. This type of neural network is formed based
on cells that perform sequential and stale calculations via gates and states
without knowing whether their order, within the cell, is optimal. In this
paper, we propose a revised and real-time adjusted long short-term memory cell
that selects the best gate or state as its final output. Our cell is running
under a shallow topology, has a minimal look-back period, and is trained
online. This revised cell achieves lower forecasting error compared to other
recurrent neural networks for online high-frequency trading forecasting tasks
such as the limit order book mid-price prediction as it has been tested on two
high-liquid US and two less-liquid Nordic stocks.

本文介绍了一种修订的在线长短时记忆细胞，该细胞运行在浅拓扑上，具有最小的回看期，通过选择最佳门或状态作为其最终输出，比其他递归神经网络更适用于在线高频交易预测任务，如限价单簿的中间价格预测。在两只高流动性的美国和两只不太流动的北欧股票上对其进行了测试。

用于高频交易预测的最佳输出长短期记忆细胞

Optimum Output Long Short-Term Memory Cell for High-Frequency Trading  Forecasting

Predicting trends in stock market prices has been an area of interest for
researchers for many years due to its complex and dynamic nature. Intrinsic
volatility in stock market across the globe makes the task of prediction
challenging. Forecasting and diffusion modeling, although effective can't be
the panacea to the diverse range of problems encountered in prediction,
short-term or otherwise. Market risk, strongly correlated with forecasting
errors, needs to be minimized to ensure minimal risk in investment. The authors
propose to minimize forecasting error by treating the forecasting problem as a
classification problem, a popular suite of algorithms in Machine learning. In
this paper, we propose a novel way to minimize the risk of investment in stock
market by predicting the returns of a stock using a class of powerful machine
learning algorithms known as ensemble learning. Some of the technical
indicators such as Relative Strength Index (RSI), stochastic oscillator etc are
used as inputs to train our model. The learning model used is an ensemble of
multiple decision trees. The algorithm is shown to outperform existing algo-
rithms found in the literature. Out of Bag (OOB) error estimates have been
found to be encouraging. Key Words: Random Forest Classifier, stock price
forecasting, Exponential smoothing, feature extraction, OOB error and
convergence.

使用随机森林分类器等机器学习算法，通过对技术指标进行特征筛选，进行股价预测，预测效果优于已有文献中现有的算法。