Python Power: Unleash Time Series Analysis Magic-Time Series Analysis with Python

Example

Filling missing values using forward fill or interpolation, converting date columns to datetime objects.

Scenario

Preparing stock market data for analysis by ensuring there are no gaps in the date sequence and that all timestamps are in the correct format.

Example

Applying seasonal_decompose from statsmodels to decompose a time series into trend, seasonal, and residual components.

Scenario

Analyzing retail sales data to understand underlying trends, seasonal effects, and irregular components for better inventory management.

Example

Using the Augmented Dickey-Fuller test from statsmodels to test for stationarity.

Scenario

Evaluating currency exchange rates to determine if they are suitable for time series forecasting or if they need differencing to achieve stationarity.

Example

Creating time series plots, seasonal plots, and autocorrelation plots with Matplotlib and Seaborn.

Scenario

Visualizing electricity consumption over time to identify usage patterns and peak demand periods.

Example

Implementing ARIMA/SARIMA models from statsmodels for forecasting.

Scenario

Forecasting future stock prices or sales numbers based on historical data to guide investment or business decisions.