Time Series Master-time series analysis tool

Example

Using pandas to handle missing values, parse dates, and set the correct index for a DataFrame.

Scenario

A financial analyst needs to prepare historical stock price data for analysis. Time Series Master can fill in missing prices and ensure dates are correctly formatted and indexed.

Example

Using matplotlib to plot time series data and observe patterns.

Scenario

An energy company wants to understand seasonal usage patterns of electricity. Time Series Master can create visual plots that reveal these patterns over time.

Example

Applying the Augmented Dickey-Fuller test to check for stationarity in a time series.

Scenario

A climatologist studying temperature data needs to ensure the data is stationary before applying predictive models. Time Series Master performs the necessary tests and suggests transformations if needed.

Example

Using statsmodels to decompose a time series into trend, seasonal, and residual components.

Scenario

A retailer wants to distinguish between overall sales growth and seasonal fluctuations. Time Series Master decomposes the sales data to reveal these components.

Example

Creating ACF and PACF plots to identify the presence of autocorrelation in the data.

Scenario

An economist analyzing GDP data needs to understand the dependencies in the data to build an ARIMA model. Time Series Master provides autocorrelation plots to guide the model selection.

Example

Creating lagged variables and moving averages for use in models like XGBoost.

Scenario

A data scientist developing a machine learning model to predict product demand can use Time Series Master to generate relevant features from historical sales data.