My journey through the data science - by Karolina M'Goma

Let's use some of the statistics I mentioned before to describe some Time Series. We can start with Dow Jones dataset which are in fpp library.  Dataset containing the Dow Jones Index is a stock market index that measures the stock performance of 30 large companies listed on stock  exchanges in the United States. library(fpp) dowjones # It is our dataset, which has class ts so we don't have to convert it.  Time Series: Start = 1  End = 78  Frequency = 1   [1] 110.94 110.69 110.43 110.56 110.75 110.84 110.46 110.56 110.46 110.05 109.60 109.31 109.31 109.25 [15] 109.02 108.54 108.77 109.02 109.44 109.38 109.53 109.89 110.56 110.56 110.72 111.23 111.48 111.58 [29] 111.90 112.19 112.06 111.96 111.68 111.36 111.42 112.00 112.22 112.70 113.15 114.36 114.65 115.06 [43] 115.86 116.40 116.44 116.88 118.07 118.51 119.28 119.79 119.70 119.28 119.66 120.14 120.97 121.13 [57] 121.55 121.96 122.26 123.79 124.11 124.14 123.37 123.02 122.86 123.02 123.11 123.05 123.05...

What we can say about the time series data at the beginning? How we can describe it and what elements determinate the method we will use to forecast the data? For my own personal use I have prepared some notes which help me to answer questions above. I was using some definitions from the book of "Forecasting: Principles & Practice" by Rob J Hyndman like also some other blog's article like: https://towardsdatascience.com/descriptive-statistics-in-time-series-modelling Basic Statistics for Time Series When you make sure that your data has time series class, you can check the data with the basic functions we have in R. ts() is useful to build Time Series from scratch. mean() shows the average of a set of data. median() shows the middle value of the arranged set of data. plot() shows on the graph how the Time series looks like sort() sort the data quantile() function returns quantiles which are cut points dividing the range of a probability distribution into continuous ...