Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Time Series Analysis-Based Stock Price Prediction Using Machine
  Learning and Deep Learning Models   Prediction of future movement of stock prices has always been a challenging
task for the researchers. While the advocates of the efficient market
hypothesis (EMH) believe that it is impossible to design any predictive
framework that can accurately predict the movement of stock prices, there are
seminal work in the literature that have clearly demonstrated that the
seemingly random movement patterns in the time series of a stock price can be
predicted with a high level of accuracy. Design of such predictive models
requires choice of appropriate variables, right transformation methods of the
variables, and tuning of the parameters of the models. In this work, we present
a very robust and accurate framework of stock price prediction that consists of
an agglomeration of statistical, machine learning and deep learning models. We
use the daily stock price data, collected at five minutes interval of time, of
a very well known company that is listed in the National Stock Exchange (NSE)
of India. The granular data is aggregated into three slots in a day, and the
aggregated data is used for building and training the forecasting models. We
contend that the agglomerative approach of model building that uses a
combination of statistical, machine learning, and deep learning approaches, can
very effectively learn from the volatile and random movement patterns in a
stock price data. We build eight classification and eight regression models
based on statistical and machine learning approaches. In addition to these
models, a deep learning regression model using a long-and-short-term memory
(LSTM) network is also built. Extensive results have been presented on the
performance of these models, and the results are critically analyzed.

5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
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The categories of this paper are: math
23
The categories of this paper are: solv-int
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The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
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The categories of this paper are: hep-ph