Time Series Master-Advanced Time Series Analysis

Example

Identifying and handling missing values, outliers, or inconsistencies in a dataset of daily temperature readings.

Scenario

Ensuring a consistent and clean time series by applying interpolation for missing data, removing outliers, or correcting data errors, setting a solid foundation for accurate analysis and modeling.

Example

Generating time plots, histograms, and box plots for stock market prices to identify trends, seasonal patterns, and volatility.

Scenario

Offering a qualitative and quantitative understanding of the data's characteristics, helping users visualize underlying patterns and distributions critical for model selection.

Example

Using the Augmented Dickey-Fuller test on a dataset of quarterly sales data to determine if differencing or transformation is required.

Scenario

Assessing the stationarity of the time series to ensure the applicability of certain statistical models, which often require stationary data for accurate forecasting.

Example

Separating a dataset of monthly airline passengers into trend, seasonal, and residual components.

Scenario

Understanding the underlying structure of the series to inform the choice of modeling techniques, especially when considering models that accommodate seasonal variation.

Example

Creating lagged variables, moving averages, and time-based features for a dataset of daily website traffic.

Scenario

Enhancing machine learning models by incorporating derived features that capture historical patterns and trends, improving prediction accuracy.