Time Series Forecasting

Seasonal, Trending, Cyclical or Random?

Notwithstanding the time series analysis is widely implemented for the business and sociology, it is still considered as one of the areas where most of the data scientists and machine learning engineers are undertaking. The accuracy of a time series forecasting model still highly depends on the degree of coverages of the real factors taken into consideration while modelling. This is the main reason why Stephen Hawking and Ransom Riggs poured some cold water on this, where currently the time series forecasting models are still not robust enough.

Nevertheless, some communities benefit from this technology. The investors perform time series analysis to figure out the opportunity to step into the stock market. The manufacturing industries use time series analysis to carry out the yield projection, inventory study and workload projection. In the medical field, doctors study the signals’ trends generated by human bodies for treatment. Census Analysis is frequently carried out by the governments to understand their citizens well [1].

Let’s start with the examination of the dataset that we used for regression and classification. It is known as cross-sectional data. It is a type of data collected by observing many subjects (such as individuals, firms, countries, or region) at one point or period of time. It is used to compare different subjects [2]. Time series data is recorded based on time intervals where its order is vital. Thus, any predictive model that is primarily based on time collection statistics contains time as an impartial variable.

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