TIME SERIES ANALYSIS AND FORECASTING USING MACHINE LEARNING

In this day and age, we are completely submerged in a world in which data is universal and essential owing to the fact that information is readily available in large quantities. An extensive variety of methods, ranging from the efforts of private individuals to those of official agencies, have been utilised in order to successfully collect the information requested. Following the categorization of the information into categories, the information is next evaluated in accordance with those categories, with the intention of getting a more exact grasp of the categories. Time series and cross-sectional data are the kind of data that are utilised the most frequently and are frequently seen as being the most relevant. This is despite the fact that there are many other forms of data. When it comes to cross-sectional data, the collection of data takes place at a certain point in time, and the variables that are the focus of this type of data are a number of variables that are of special interest. Take, for instance, the case of a manager of a retail shop who compiled the sales of four different varieties of ice cream throughout the course of the previous month of the previous month. Specifically, this specific dataset will be cross-sectional, and it will be comprised of four numbers: the total sales for each ice cream product over the course of a single month. When it comes to the order in which the cross-sectional data are presented, there is also a certain degree of freedom that may be utilised. The information can be communicated through the use of a form that is either ascending, descending, or the random form. It's possible that all three of these types may exist. On the other hand, cross-sectional data and time series data are quite distinct from one another in a number of major aspects at the same time. There is a temporal order that is inherent to everything that is contained inside the data that makes up a time series.