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Time-series Forecasting of Bitcoin Prices and Illiquidity
using High-dimensional Features: XGBoostLSTM
Approach

Corresponding author: Ramin Mousa

Abstract Liquidity is the ease of converting an asset into cash or another asset
without loss, and is shown by the relationship between the time scale and the
price scale of an investment. This article examines the relationship between
Bitcoin’s price prediction and illiquidity. Bitcoin Hash Rate information was col-
lected in three different intervals, and three techniques of feature selection (FS)
Filter, Wrapper, and Embedded were used. Considering the regression nature of
illiquidity prediction, an approach based on LSTM network and XGBoost was
proposed. LSTM was used to extract time series features, and XGBoost was used
to learn these features. The proposed LSTMXGBoost approach was evaluated in
two modes: price prediction and illiquidity prediction. This approach achieved
MAE 1.60 in the next-day forecast and MAE 3.46 in the next-day illiquidity
forecast. In the cross-validation of the proposed approach on the FS approaches,
the best result was obtained in the prediction by the filter approach and in
the classification by the wrapper approach. These obtained results indicate that
the presented models outperform the existing models in the literature. Examin-
ing the confusion matrices indicates that the two tasks of price prediction and
illiquidity prediction have no correlation and harm each other.

Keywords: illiquidity prediction, Bitcoin hash rate, hybrid model, price pre-
diction, LSTMXGBoost
ژورنال سابمیت
Journal : Finanace innovation(springer)
If: 6.5
دوستانی که در سری زمانی کار می کنن می تونن در این مقاله شرکت کنن.

@Raminmousa

👍3

www.tgoop.com/Machine_learn/3395

1.88K viewsFeb 10 at 15:35

با عرض سلام برای یکی از مقالاتمون نفر دوم رو لازم داریم زمان سابمیت امشب تا فردا شب

Time-series Forecasting of Bitcoin Prices and Illiquidity
using High-dimensional Features: XGBoostLSTM
Approach

Corresponding author: Ramin Mousa

Abstract Liquidity is the ease of converting an asset into cash or another asset
without loss, and is shown by the relationship between the time scale and the
price scale of an investment. This article examines the relationship between
Bitcoin’s price prediction and illiquidity. Bitcoin Hash Rate information was col-
lected in three different intervals, and three techniques of feature selection (FS)
Filter, Wrapper, and Embedded were used. Considering the regression nature of
illiquidity prediction, an approach based on LSTM network and XGBoost was
proposed. LSTM was used to extract time series features, and XGBoost was used
to learn these features. The proposed LSTMXGBoost approach was evaluated in
two modes: price prediction and illiquidity prediction. This approach achieved
MAE 1.60 in the next-day forecast and MAE 3.46 in the next-day illiquidity
forecast. In the cross-validation of the proposed approach on the FS approaches,
the best result was obtained in the prediction by the filter approach and in
the classification by the wrapper approach. These obtained results indicate that
the presented models outperform the existing models in the literature. Examin-
ing the confusion matrices indicates that the two tasks of price prediction and
illiquidity prediction have no correlation and harm each other.

Keywords: illiquidity prediction, Bitcoin hash rate, hybrid model, price pre-
diction, LSTMXGBoost
ژورنال سابمیت
Journal : Finanace innovation(springer)
If: 6.5
دوستانی که در سری زمانی کار می کنن می تونن در این مقاله شرکت کنن.

@Raminmousa

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