Development of a clusterwise-linear-regression-based forecasting system for characterizing DNAPL dissolution behaviors in porous media

2012 ◽  
Vol 433 ◽  
pp. 141-150 ◽  
Author(s):  
S. Wang ◽  
G.H. Huang ◽  
L. He
Keyword(s):  
Porous Media ◽  
Linear Regression ◽  
Forecasting System ◽  
Dnapl Dissolution ◽  
Clusterwise Linear Regression

scholarly journals Forecasting Bitcoin Trends Using Algorithmic Learning Systems

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 838
Author(s):  
Gil Cohen
Keyword(s):  
Linear Regression ◽  
Efficient Market Hypothesis ◽  
Swarm Optimization ◽  
Long Period ◽  
Forecasting Methods ◽  
Price Trends ◽  
Regression Techniques ◽  
Forecasting System ◽  
Algorithmic Learning ◽  
Daily Returns

This research has examined the ability of two forecasting methods to forecast Bitcoin’s price trends. The research is based on Bitcoin—USA dollar prices from the beginning of 2012 until the end of March 2020. Such a long period of time that includes volatile periods with strong up and downtrends introduces challenges to any forecasting system. We use particle swarm optimization to find the best forecasting combinations of setups. Results show that Bitcoin’s price changes do not follow the “Random Walk” efficient market hypothesis and that both Darvas Box and Linear Regression techniques can help traders to predict the bitcoin’s price trends. We also find that both methodologies work better predicting an uptrend than a downtrend. The best setup for the Darvas Box strategy is six days of formation. A Darvas box uptrend signal was found efficient predicting four sequential daily returns while a downtrend signal faded after two days on average. The best setup for the Linear Regression model is 42 days with 1 standard deviation.

Algorithm 39 Clusterwise linear regression

Computing ◽  
1979 ◽  
Vol 22 (4) ◽  
pp. 367-373 ◽  
Author(s):  
H. Späth
Keyword(s):  
Linear Regression ◽  
Clusterwise Linear Regression
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