Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to sugg...

This study investigates the effects of varying sampling intervals on the long memory characteristics of certain stochastic processes. We find that although different sampling intervals do not affect the decay rate of discrete time long memory autocorrelation functions in large lags, the autocorrelation functions in short lags are affected significantly. The level of the autocorrelation function...

Recently, the visibility graph has been introduced as a novel view for analyzing time series, which maps it to a complex network. In this paper, we introduce a new algorithm of visibility, ”cross-visibility”, which reveals the conjugation of two coupled time series. The correspondence between the two time series is mapped to a network, ”the cross-visibility graph”, to demonstrate the correlatio...

What dynamics govern a time series representing the appearance of words in social media data? In this paper, we investigate an elementary dynamics, from which word-dependent special effects are segregated, such as breaking news, increasing (or decreasing) concerns, or seasonality. To elucidate this problem, we investigated approximately three billion Japanese blog articles over a period of six ...

A moment bound for the normalized conditional-sum-of-squares (CSS) estimate of a general autoregressive fractionally integrated moving average (ARFIMA) model with an arbitrary unknown memory parameter is derived in this paper. To achieve this goal, a uniform moment bound for the inverse of the normalized objective function is established. An important application of these results is to establis...

Currently the emergence of novel coronavirus (Sars-Cov-2), which causes COVID-19 pandemic and has become a serious health problem because high risk death. Therefore, fast appropriate action is needed to reduce spread pandemic. One way build prediction model so that it can be reference in taking steps overcome them. Because nature transmission this disease massive cause extreme data fluctuations...

We introduce a method for reconstructing macroscopic models of one-dimensional stochastic processes with long-range correlations from sparsely sampled time series by combining fractional calculus and discrete-time Langevin equations. The is illustrated the ARFIMA(1,d,0) process nonlinear autoregressive toy model multiplicative noise. reconstruct daily mean temperature data recorded at Potsdam, ...