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Table 5 Parameter estimates for the time series seen in Figs. 5 and 6

From: Using correlated stochastic differential equations to forecast cryptocurrency rates and social media activities

 

\(\hat {\bar {\theta }}\)

\(\hat {\sigma }\)

\(\hat {\kappa }\)

(C−1)n,n

Twitter

April

19063

0.142

0.0103

1.26

 

June

22878

0.150

0.0129

1.41

 

August

24746

0.510

0.140

1.10

Reddit

April

2790

0.216

0.0338

1.18

 

June

3944

0.214

0.0340

1.24

 

August

4397

0.174

0.0238

1.13

Github

April

1337

0.309

0.0879

1.19

 

June

1660

0.326

0.0947

1.32

 

August

1651

0.278

0.0758

1.17

 

\(\hat {\bar {\mu }}\)

\(\hat {\sigma }\)

 

(C−1)n,n

Bitcoin

April

0.00223

0.0404

 

1.18

 

June

0.00801

0.0393

 

1.15

 

August

0.01158

0.0484

 

1.36

Ethereum

April

0.0196

0.0692

 

1.32

 

June

0.0308

0.0802

 

1.34

 

August

0.0179

0.0802

 

1.40

  1. Each three rows correspond to a different time series and each row in that subset correspond to the prediction in April, June, and August of 2017. As can be seen there is typically small changes over time for each parameter