for prediction/forecasting use cases is termed as time series forecasting though in generalboth schools of thought utilize the same set of tools and techniques it is more about how the concepts are grouped for structured learning and application ##note time series and its analysis is complete field of study on its own and we merely discuss certain concepts and techniques for our use cases here this is by no means complete guide on time series and its analysis time series can be analyzed both in frequency and time domains the frequency domain analysis includes spectral and wavelet analysis techniqueswhile time domain includes autoand cross-correlation analysis in this we primarily focus on time series forecasting in the time domain with brief discussion around descriptive characteristics of time series data moreoverwe concentrate on univariate time series analysis (time is an implicit variableto better understand concepts related to time serieswe utilize sample dataset the following snippet loads web site visit data with daily frequency using pandas you can refer to the jupyter notebook notebook_getting_started_time_series ipynb for the necessary code snippets and examples the dataset is available at in [ import pandas as pd #load data input_df pd read_csv( 'website-traffic csv'input_df['date_of_visit'pd to_datetime(input_df monthday str catinput_df year astype(str)sep=')the snippet first creates new attribute called the date_of_visit using combination of daymonthand year values available in the base dataframe since the dataset is about web site visits per daythe variable of interest is the visit count for the day with the time dimensioni date_of_visit being the implicit one the output plot of visits per day is shown in figure -