1. Data evaluation and elementary preprocessing. Analyse completeness of data. Are there missedata (besides weekends)? How many missed data points are in your time series? Are the dates ofmissed values the same for all your time series? What may be the reasons for missing? How can youhandle the missed values in your data (explain at least three approaches)? Use the simple rule: fill in amissed value by the closest in time past existing value. Plot the results. Normalise to the z-score (zeromean and unit standard deviation). Plot the results. (15 marks)3. Segmentation. Prepare the bottom-up piecewise linear segmentation for the transformed andnormalised log-return time series. Use the following mean square errors tolerance levels: 1%, 5%, 10%(the thresholds of the mean square errors). Plot the results. Are the segments similar for different timeseries you analysed? (25 marks)For more information on Elementary Preprocessing check on: https://en.wikipedia.org/wiki/Digital_image_processing
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