What is a trend in time series? - GeeksforGeeks (2024)

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Last Updated : 20 Mar, 2024

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Time series data is a sequence of data points that measure some variable over ordered period of time. It is the fastest-growing category of databases as it is widely used in a variety of industries to understand and forecast data patterns. So while preparing this time series data for modeling it’s important to check for time series components or patterns. One of these components is Trend.

Trend is a pattern in data that shows the movement of a series to relatively higher or lower values over a long period of time. In other words, a trend is observed when there is an increasing or decreasing slope in the time series. Trend usually happens for some time and then disappears, it does not repeat. For example, some new song comes, it goes trending for a while, and then disappears. There is fairly any chance that it would be trending again.

A trend could be :

Uptrend: Time Series Analysis shows a general pattern that is upward then it is Uptrend.
Downtrend: Time Series Analysis shows a pattern that is downward then it is Downtrend.
Horizontal or Stationary trend: If no pattern observed then it is called a Horizontal or stationary trend.

You can find trends in data either by simply visualizing or by the decomposing dataset.

Visualization

By simply plotting the dataset you can see the general trend in data

Approach :

Import module
Load dataset
Cast month column to date time object
Set month as index
Create plot

Note: In the examples given below the same code is used to show all three trends just the dataset used is different to reflect that particular trend.

Example: Uptrend

Python3

# importing the libraries

import pandas as pd

import matplotlib

# importing dataset

data = pd.read_csv(r'C:\Users\admin\Downloads\Electric_Production.csv')

# casting Month column to datetime object

data['DATE'] = pd.to_datetime(data['DATE'])

# Setting Month as index

data = data.set_index('DATE')

# Creating the plot

data.plot()

Python3

import pandas as pd

import matplotlib

# importing dataset

data = pd.read_csv(r'C:\Users\admin\Downloads\AlcoholSale.csv')

# casting Date column to datetime object

data['DATE'] = pd.to_datetime(data['DATE'])

# Setting Date column as index

data = data.set_index('DATE')

# Creating the plot

data.plot()

Output :

Example: Horizontal trend

Python3

# importing the libraries

import pandas as pd

import matplotlib

# importing dataset

data = pd.read_csv(

r'C:\Users\admin\Downloads\monthly-beer-production-in-austr.csv')

# casting Month column to datetime object

data['Month'] = pd.to_datetime(data['Month'])

# Setting Month as index

data = data.set_index('Month')

# Creating the plot

data['1984':'1994'].plot()

Python3

# importing function

from statsmodels.tsa.seasonal import seasonal_decompose

# creating trend object by assuming multiplicative model

output = seasonal_decompose(data, model='multiplicative').trend

# creating plot

output.plot()

Output :

tejalkadam18m

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