In Python, which library is widely used for time series analysis and forecasting?

The correct answer is D. Statsmodels.

Statsmodels is a Python module that provides a variety of statistical modeling and analysis tools. It is widely used for time series analysis and forecasting.

Numpy is a Python module that provides a high-performance array manipulation library. It is not specifically designed for time series analysis, but it can be used for some tasks.

Matplotlib is a Python module that provides a plotting library. It can be used to create plots of time series data, but it is not specifically designed for time series analysis.

Pandas is a Python module that provides a high-performance data analysis library. It can be used for some tasks in time series analysis, but it is not specifically designed for that purpose.

Here are some additional details about each option:

• Numpy is a Python module that provides a high-performance array manipulation library. It is not specifically designed for time series analysis, but it can be used for some tasks. For example, Numpy can be used to create a time series by stacking a sequence of 1D arrays. Numpy can also be used to perform basic statistical operations on time series data, such as calculating the mean, standard deviation, and correlation.
• Matplotlib is a Python module that provides a plotting library. It can be used to create plots of time series data, but it is not specifically designed for time series analysis. Matplotlib can be used to plot time series data in a variety of ways, including line plots, bar charts, and scatter plots. Matplotlib can also be used to add annotations to time series plots, such as trend lines and error bars.
• Pandas is a Python module that provides a high-performance data analysis library. It can be used for some tasks in time series analysis, but it is not specifically designed for that purpose. Pandas can be used to read and write time series data from a variety of sources, including CSV files, Excel spreadsheets, and databases. Pandas can also be used to perform basic statistical operations on time series data, such as calculating the mean, standard deviation, and correlation. Pandas can also be used to create time series models, such as ARIMA models and exponential smoothing models.
• Statsmodels is a Python module that provides a variety of statistical modeling and analysis tools. It is widely used for time series analysis and forecasting. Statsmodels includes a number of functions and classes that are specifically designed for time series analysis, such as the tsa.ARIMA class and the tsa.ExponentialSmoothing class. Statsmodels also includes a number of functions and classes that are useful for forecasting, such as the forecasting.Forecast class and the forecasting.ARIMAForecast class.