Bootstrapping is a resampling method that allows us to estimate the sampling distribution of a statistic using the observed data. In the case of a time series dataset, where there is a temporal dependency between observations, we need to take this into account when performing bootstrapping.

Here is a step-by-step guide to perform bootstrapping and estimate the 95% confidence interval for the mean of a time series dataset:

1. Define your time series dataset: The first step is to have a time series dataset ready. Make sure it is properly formatted with the time index and the corresponding values.

2. Define the number of resamples: Decide on the number of resamples you want to perform. The more resamples you make, the more accurate the estimation will be, but it also increases computational time. A common choice is a few thousand resamples.

3. Perform block or stationary bootstrapping: As mentioned earlier, time series data has temporal dependencies, and simple random sampling would break these dependencies. There are two popular methods to address this issue:
a. Block bootstrapping: In this method, we divide the time series into blocks of a fixed length and resample complete blocks rather than individual observations.
b. Stationary bootstrapping: In this method, we resample the residuals, which are obtained by fitting a suitable time series model (e.g., ARIMA) to the original data. The residuals help capture the temporal dependencies.

4. Resample and calculate means: For each resample, randomly select blocks (or residuals) from the original dataset with replacement. Calculate the mean of each resample.

5. Calculate the confidence interval: Once you have the means for each resample, sort them in ascending order. To estimate the confidence interval, use the 2.5th and 97.5th percentiles of the sorted means. These percentiles mark the lower and upper bounds of the 95% confidence interval.

6. Report the confidence interval: Finally, report the estimated 95% confidence interval for the mean of the time series dataset.

Note: While bootstrapping can provide robust estimates for the mean of a time series dataset, it is important to consider the underlying assumptions and limitations of the method. Additionally, suitable adjustments may be required based on the specific characteristics of the dataset (e.g., nonstationarity, seasonality) and the objective of the analysis.

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