By default, data in the format of “yyyy-mm-dd HH: mm: SS” is read. Date parse: specifies a function that converts the input string to a datetime variable.So this parameter tells panda to use the ‘month’ column as the index. index\_ Col: a key idea behind using pandas for TS data is that the index must be a variable that describes the date and time information.As mentioned above, the column name is’ month ‘. parse Dates: Specifies the column containing date time information.Let’s understand these arguments one by one Let’s start by starting the required libraries: %matplotlib inlineĭata = pd.read_csv('AirPassengers.csv', parse_dates=, index_col='Month') Pandas has a special library to handle TS objects, especially the datatime64 class, which stores time information, and we can perform some operations quickly. Let’s start by loading a TS object in Python. These issues are discussed in detail below. For example, if you see the sales of a wool jacket over time, you will definitely find that sales are higher in winter.īecause of its inherent characteristics, the analysis involves various steps. With an increasing or decreasing trend, most ts have some form of seasonal trend, that is, changes specific to a specific time range.Therefore, the basic assumption of linear regression model, that is, the observation value is independent, does not hold in this case. These data are analyzed to determine long-term trends and to predict the future or other forms of analysis.īut what makes TS different from the conventional regression problem?
EXPONENTIALLY WEIGHTED STANDARD DEVIATION HOW TO