  We propose two different approaches for introducing the information
temperature of the binary N-th order Markov chains. The first approach is based
on comparing the Markov sequences with the equilibrium Ising chains at given
temperatures. The second approach uses probabilities of finite-length
subsequences of symbols occurring, which determine their entropies. The
derivative of the entropy with respect to the energy gives the information
temperature measured on the scale of introduced energy. For the case of
nearest-neighbor spin/symbol interaction, both approaches provide similar
results. However, the method based on the correspondence of the N-step Markov
and Ising chains appears to be very cumbersome for N>3. We also introduce the
information temperature for the weakly correlated one-parametric Markov chains
and present results for the step-wise and power memory functions. An
application of the developed method to obtain the information temperature of
some literary texts is given.
