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Time Series Analysis with Python Libraries – Data Patrons

Time Series Analysis Overview

Time series analysis is a statistical technique for analysing and modelling time-dependent data. This type of data is common in many sectors, including economics, finance, medical, and engineering. Time series data is distinguished by its temporal nature, with measurements made at regular intervals over time. The basic goal of time series analysis is to find patterns in data, estimate future values, and construct models that can explain the time series’ underlying mechanisms.

Python Time Series Analysis Libraries

Python for data science in NCR is an open-source programming language that has evolved into the industry standard for data science and machine learning. There are several Python libraries for time series analysis, making it easier for data scientists and analysts to work with time series data. Some of the most popular Python libraries for time series analysis are:

Pandas – Pandas is a data structure library for efficiently handling and manipulating time series data. It also has resampling, interpolation, and time zone handling functions.

NumPy – is a library that supports efficient numerical computations, such as time series data processing. It contains mathematical procedures, statistical analysis, and linear algebra functions.

Matplotlib – Matplotlib is a visualisation package used in time series analysis to create graphs and charts that display time series data.

StatsModels – is a library that includes a variety of statistical models for time series analysis, such as ARIMA, SARIMA, and VAR models.

Prophet – Prophet is a time series forecasting library created by Facebook. It develops models that can handle various time series patterns, such as seasonality and trend, using a Bayesian method.     

Python Time Series Data Analysis

The steps for analysing time series data with Python are as follows:

Step 1: Gathering Data

Preparing the Deep python course training institute in NCR data is the initial stage in time series analysis. This entails loading the data into a Pandas data frame and configuring the time series index. A datetime object representing the time of each observation should be used as the time series index. Pandas has several functions for dealing with time series data, including resampling, shifting, and rolling.

Step 2: Visualization of Data

The time series data will now be visualised. Visualization is a key stage in time series research because it provides insights into the underlying patterns and trends in the data. Matplotlib is a popular package for making time series data visualisations. It has tools for making line plots, scatter plots, and bar charts.

Step 3: Analyze the Data

The next stage is to analyse the data. This entails employing statistical models to detect patterns and trends in data. Stats Models is a popular time series analysis package that includes a variety of models such as ARIMA, SARIMA, and VAR models. These models can anticipate future values, identify seasonal patterns, and discover anomalies.

Step 4: Model Assessment

The fourth step is to assess the models’ performance. The actual values of the time series data are compared to the predicted values of the models. To assess the performance of the models, metrics such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) can be utilised.

5th Step: Forecasting

The final stage is to forecast the time series data’s future values. Prophet is a popular time series forecasting library that use a Bayesian method to construct models that can handle a wide range of time series patterns, including seasonality and trend. It has functions for forecasting and visualising the results.


Time series analysis is an effective method for analyzing and modelling time-varying data. Top Python course training institute in NCR includes various time series analysis libraries, including Pandas, NumPy, and Matplotlib.