Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

CHAPTER 6
Time Series
A time series is a series of data points arranged chronologically. Most
commonly, the time points are equally spaced. A few examples are the
passenger loads of an airline recorded each month for the past two years
or the price of an instrument in the share market recorded each day for the
last year. The primary aim of time-series analysis is to predict the future
value of a parameter based on its past data.
Classification of Variation
Traditionally time-series analysis divides the variation into three major
components, namely, trends, seasonal variations, and other cyclic
changes. The variation that remains is attributed to “irregular” fluctuations
or error term. This approach is particularly valuable when the variation is
mostly comprised of trends and seasonality.
Analyzing a Series Containing a Trend
A trend is a change in the mean level that is long-term in nature. For
example, if you have a series like 2, 4, 6, 8 and someone asks you for the
next value, the obvious answer is 10. You can justify your answer by fitting
a line to the data using the simple least square estimation or any other
regression method. A trend can also be nonlinear. Figure 6-1 shows an
example of a time series with trends.
© Sayan Mukhopadhyay 2018
S. Mukhopadhyay, Advanced Data Analytics Using Python,
https://doi.org/10.1007/978-1-4842-3450-1_6
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