The present disclosure has been made in view of such a problem, and an object of the present disclosure is to make it possible to predict fluctuation of unsteady traffic with a small amount of calculation.
A traffic fluctuation prediction apparatus (91) according to the present disclosure includes: a data division unit (12) that divides time-series data XN(t) in a certain period of the traffic into estimation and prediction data sets; a learning unit (13) that learns a dictionary Dr(t) using one of the two divided data sets, and learns a dictionary Dp(t) using the other of the two divided data sets; a prediction unit (14) that obtains a sparse code YN(t) in representing the time-series data using the learned dictionary Dr(t) and obtains a predicted value XN{circumflex over ( )}(t) of future traffic using the obtained sparse code YN(t) and the dictionary Dp(t); and a prediction correction unit (15) that corrects a prediction error of the predicted value XN{circumflex over ( )}(t) obtained by the prediction unit (14) using search for a stable region of a double queue of overestimation and underestimation.