[1911.13288v1] Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019
In our survey, we wanted to review the existing studies to provide a snapshot of the current research status of DL implementations for financial time series forecasting

\begin{abstract}
Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. \gls{ml} researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering \gls{ml} for financial time series forecasting studies. Lately, \gls{dl} models started appearing within the field, with results that significantly outperform traditional \gls{ml} counterparts. Even though there is a growing interest in developing models for financial time series forecasting research, there is a lack of review papers that were solely focused on \gls{dl} for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on \gls{dl} studies for financial time series forecasting implementations. We not only categorized the studies according to their intended forecasting implementation areas, such as index, forex, commodity forecasting, but also grouped them based on their \gls{dl} model choices, such as \glspl{cnn}, \glspl{dbn}, \gls{lstm}. We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.
\end{abstract}
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Figure 5: The histogram of Publication Count in Topics (Current Snaphot of The Field)Figure 6: The rate of Publication Count in Topics (Current Snaphot of The Field)Figure 7: Topic-Model Heatmap (Current Snaphot of The Field)Figure 10: Top Journals corresponding numbers next to the bar graph are representing the impact factor of the journals (Current Snaphot of The Field)Figure 8: The histogram of Publication Count in Years (Current Snaphot of The Field)Figure 9: The histogram of Publication Count in Publication Types (Current Snaphot of The Field)Figure 13: The Preferred Development Environments (Current Snaphot of The Field)Figure 11: The Piechart of Publication Count in Model Types (Current Snaphot of The Field)Figure 12: Distribution of RNN Models (Current Snaphot of The Field)›