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I took your 29 days (oldest to newest) and found that there were 3 unusual days thus the following equation with Actual/Fit and Forecast here

All models are wrong ... but some are useful .... . It is fundamentally an autoregressive process of order 1 after one has adjusted for the three "unusual data points " see for a clear support for the anomaly identification.

Plot of the residuals from the above model suggesting reduced variability is clearly obvious . It is reasonable to suggest that there has been a break-point in the model error variance suggesting GLS or a weighted model . This was not not investigated here due to sample size ! ).

Here is the plot of the original data

I took your 29 days (oldest to newest) and found that there were 3 unusual days thus the following equation with Actual/Fit and Forecast here

All models are wrong ... but some are useful .... . It is fundamentally an autoregressive process of order 1 after one has adjusted for the three "unusual data points " see for a clear support for the anomaly identification.

Plot of the residuals from the above model suggesting reduced variability is clearly obvious . It is reasonable to suggest that there has been a break-point in the model error variance suggesting GLS or a weighted model . This was not not investigated here due to sample size ! ).

Here is the plot of the original data

While the variability of the series is higher at higher values suggesting to some that there is a need for logarithms When (and why) should you take the log of a distribution (of numbers)? ..it is truer yet that the error variance distribution is better characterized as having a deterministic change point at or about day 11.

oops you presented the data in a backwards fashion .... quite unorthodox

I will have to scrap this and resort from oldest to newest..

I took your 30 days and found that there were 3 unusual days thus the following equation with Actual/Fit and Forecast here

All models are wrong ... but some are useful .... . It is fundamentally a random walk with no drift after one has adjusted for the three "unusual data points " ... two of which are the most recent .....

Plot of the residuals from the above model suggesting increasing variability might be present ( not investigated here due to sample size ! ).

Here is the plot of the original data ... doesn't seem to resemble yours ....

I took your 29 days (oldest to newest) and found that there were 3 unusual days thus the following equation with Actual/Fit and Forecast here

All models are wrong ... but some are useful .... . It is fundamentally an autoregressive process of order 1 after one has adjusted for the three "unusual data points " see for a clear support for the anomaly identification.

Plot of the residuals from the above model suggesting reduced variability is clearly obvious . It is reasonable to suggest that there has been a break-point in the model error variance suggesting GLS or a weighted model . This was not not investigated here due to sample size ! ).

Here is the plot of the original data

I took your 30 days and found that there were 3 unusual days thus the following equation with Actual/Fit and Forecast here

All models are wrong ... but some are useful .... . It is fundamentally a random walk with no drift after one has adjusted for the three "unusual data points " ... two of which are the most recent .....

Plot of the residuals from the above model suggesting increasing variability might be present ( not investigated here due to sample size ! ).

Here is the plot of the original data ... doesn't seem to resemble yours ....

oops you presented the data in a backwards fashion .... quite unorthodox

I will have to scrap this and resort from oldest to newest..

I took your 30 days and found that there were 3 unusual days thus the following equation with Actual/Fit and Forecast here

All models are wrong ... but some are useful .... . It is fundamentally a random walk with no drift after one has adjusted for the three "unusual data points " ... two of which are the most recent .....

Plot of the residuals from the above model suggesting increasing variability might be present ( not investigated here due to sample size ! ).

Here is the plot of the original data ... doesn't seem to resemble yours ....