1. Choose an 'enviromental' time series that interests you that includes:
> 10 cycles worth of data
> 120 data points
you expect will have a trend and some seasonality
2. Import the time series into Python and convert (if necessary) into a Pandas DataFrame perform as much of the coding as you can directly on the DataFrame
3. Analyze the time series to: Detrend
Remove seasonality
Remove any additional cycles you observe
4. Check to see if your resulting data is: normal stationary still shows any cyclicity include statistics or plots that show your point
5. Explain: in a few paragraphs explain:
the trend, which might include things like the rate of change (i.e. your best fit parameters) the seasonality, which might include thigns like the period, the 'goodness of fit' and include any appropriate images/figures in your colab within the text boxes
6. Perform at least one additional test on your data. Dealer's choice.
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