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tokens: and reported that the amplitude of cycle 25 will be weaker than that of the current cycle 24. Macario-Rojas, Smith E ( 2018 ) also applied a similar model-based method and predicted the cycle 25 will be 14.4 per cent ( per cent) weaker than the cycle 24. Kakad, Kakad Ramesh ( 2017 ) used the estimation of Shannon entropy to predict a very weak solar cycle (ISSN peaks at 63), similar to th Dalton minimum. Predictions of a very weak solar cycle were also reported by Kirov et al. ( 2017 ), who used a precursor method and the original version of the ISSN. They found that the smoothed monthly mean sunspot number at the maximum of cycle 25 will be within 50–55 range. It should also be noted that the original version of the ISSN is statistically 45 per cent lower than the new version 2. Hathaway Upton ( 2016 ) and Gopalswamy et al. ( 2018 ) used a precursor method and predicted moderate amplitudes of cycle 25, similar to the current cycle 24, while Cameron, Jiang Schüssler ( 2016 ) reported that cycle 25 may slightly stronger than the current one. Pesnell Schatten ( 2018 ) also applied a precursor method by using SODA Index, which is determined by polar magnetic fields and the spectral index. They estimated the next cycle’s smoothed peak activity will be with a possibility of exceeding this early estimation due to the increasing trend of the SODA Index. Jiang Cao ( 2017 ) used a surface flux transport model-based method and concluded that cycle 25 might be stronger than cycle 24. Predictions of similar or stronger cycle 25 as compared to the current cycle 24 are in agreement with our predictions presented in this study. Precursor methods use some measure of geomagnetic activity or polar magnetic fields at a particular time to predict amplitude of the next cycle. In general, this particular time is chosen at the minimum phase of a cycle it may produce misleading results if the selection is made before the ISSN reached the minimum. To the contrary, the approach presented here can be applied at any time during the descending phase, but the starting point should be chosen carefully. There is additional advantage that this method allows us to also predict fluctuations of sunspot numbers during a solar cycle such as double or multiple maxima as can be seen in Figs 2 and 3 . These plots clearly show the accurate predictions of maximum times and amplitudes as well as main fluctuations occurred during solar cycles. Further analysis showed that apart from the embedding dimension and delay-times, performance of this non-linear method is highly dependent on the starting point of the prediction. This is mostly because the algorithm searches for nearest neighbours to the prediction vector, which is formed from the last (( m *τ))) +1 points before the starting point. In this case, the consecutive starting points may yield different prediction vectors and eventually different outcomes. Another explanation for the starting point dependence can be made by analysing