Patterns and Characteristics of Time Series: An Analysis

Running head: STATISTICS 1
DB 5
Student’s name
University affiliation
Author’s note

STATISTICS 2
DB 1 SUMMARY
These discuses about the patterns and characteristics of time series. Time series is data
that has been observed and recorded over a specific period. It is used when remarks are made
constantly over 50 or more periods. The remarks can be from a single case, but in most cases, it
is derived from multiple instances. The main goal of the analysis is to categorize patterns in the
order of numbers over time, which are interrelated, but offset in time.
The analysis involves three steps, identifying, estimating, and diagnosis. Identification
involves examination of the Partial Autocorrelation Functions (PACFs) and Autocorrelation
Functions (ACFs) (Dalinina, 2017). Long-time series have tendencies for measures, which vary
periodically and are referred to seasonality.
Time series analysis is the most applicable autocorrecting analysis for data as compared
to multiple regressions. First, there is an unequivocal violation of the assumption of
independence error. Secondly is that the patterns are either incomprehensible or spuriously
develop the effect of an intervention lest accounted for in the model.
DB 2 SUMMARY
This discusses and analyses the significance and prominence of data preparation with
regards to time series analysis. It is essential to prepare data before analyzing and getting it to a
comfortable set, which can be read and interpreted (Dalinina, 2017). Data preparation involves
gathering raw data from various sources, combining and reefing it so that it can be adjusted and
organized for easy analysis. This can be done by IT specialists or in business departments.
Data preparation is necessary due to multiple reasons. Information obtained might be in
different formats hence essential to group them appropriately. Also, the data would not found in
one location, thus the need for preparation. Lastly, it avoids discrepancies, errors, and lack of