Data Preparation in Time Series Analysis: Purpose, Importance

This essay explores the critical role of data preparation in time series analysis, emphasizing its importance in ensuring data quality and accuracy for effective forecasting and decision-making. It highlights how data preparation involves collecting, cleaning, integrating, and processing data to identify trends and patterns over time. The essay discusses how time series analysis uses these prepared data to understand underlying forces driving specific trends, enabling businesses to react positively to market changes and make informed decisions. It also addresses common challenges in data preparation, such as inconsistent data formats and limited data access, and underscores the necessity of addressing these issues to achieve reliable outcomes in time series analysis. The document emphasizes that Desklib offers solved assignments for students.

Running head: DATA PREPERATION 1
Data preparation
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DATA PREPERATION 2
Introduction
Data preparation is a method that involves collecting, consolidating, integrating cleaning
and processing data to use in the analysis (Zhang, Zhang & Yang2013). Data preparation
transforms the data, improves its outcome’s accuracy and enriches it. Time series analysis deals
with trend analysis. It involves the collection of data over a period of time at specific intervals to
identify seasonal variances, trends and cycles to help in forecasting future events. Data
preparation extracts strategic insights from the data which time series analysis integrates to
translate into actionable decisions.
The purpose and importance of data preparation with regards to time series analysis
Data preparation uses the statistical technique of time series analysis to help people
understand the forces which lead to specific trends in data points in time series (Lo, 2016). It fits
the appropriate models in data points to assist in monitoring and forecasting them. Time series
preparation and time series analysis is vital in monitoring and tracking corporate and industrial
business metrics. They add value to a business as it helps it to react positively to trends in the
market and influences it to make the right decisions.
Data preparation involves reformatting datasets to ensure high-quality data in time series
analysis. It leads to timely, better, efficient and high-quality decisions. Data preparation helps
managers to develop objectives and goals revealed by time series analysis. The prepare data to
use in forecasting by comparing with historical data points. It allows people to cope with
uncertainties, which may come in the future. It is essential in making short term and long term
decisions based on the data’s historical patterns.

DATA PREPERATION 3
The purpose of data preparation is to generate insights to be used as a basis for decision making.
It uses time series analysis to identify the nature of the phenomenon and predict future values.
Data preparation is important in generalization, normalization and aggregation of data for time
series analysis. It also reduces data presentation and handles redundancies to yield more accurate
outcomes in the analysis (Martínez & de Prada, 2017). It corrects inconsistent or duplicate data
and fills missing values.
Indications that dataset needs additional preparation
Datasets which have inconsistent data, missing values, duplicate records, and random
errors need data cleaning as additional data preparation (Aitchison, 2012). Redundancies in data
and conflicts are indicators of data which requires additional data preparation of integration.
Datasets which cannot be generalized summarized reduced and normalized is another indicator.
A dataset which does not have the three attributes including ordinal, continuous and nominal
requires additional data preparation. Data with mistakes from human errors in data input, faulty
sensors, data storage and transfer systems leading to corrupt data needs additional preparation.
Conclusion
Data preparation aims to ensure analytics process get data which does not have errors and
which is consistent to be able to understand, read and work with it. Time series analysis works
with the data by using patterns to predict future changes and behaviors. The common data
preparation challenges are inconsistency, multiple data formats, lack of infrastructures to
integrate data and large or limited data access. The main purpose of data preparation with regards
to time series analysis is to collect correct data to make decisions based on prior patterns.

DATA PREPERATION 4
References
Aitchison, J. (2012). The statistical analysis of compositional data. Journal of the Royal
Statistical Society: Series B (Methodological), 44(2), 139-160.
Lo, L. L. (2016). Time series analysis and person-specific psychological development: State
space modeling applications in behavior genetic and neurocognitive designs. 8(5), 604-
721.
Martínez, E., & de Prada, C. (2017). Control loop performance assessment using ordinal time
series analysis. In Computer Aided Chemical Engineering (Vol. 24, pp. 261-266).
Elsevier.
Zhang, S., Zhang, C., & Yang, Q. (2013). Data preparation for data mining. Applied artificial
intelligence, 17(5-6), 375-381.