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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