Jet Time Series Forecaster-Time Series Forecasting

Example

Displaying historical sales data in a line graph to identify trends, seasonality, and outliers.

Scenario

A user uploads monthly sales data, and Jet Time Series Forecaster generates a line graph. This visualization helps in quickly identifying patterns, such as peak sales periods during holidays, which are crucial for planning inventory and marketing strategies.

Example

Conducting tests to check if the dataset is stationary, which is a prerequisite for many time series forecasting models.

Scenario

Before building a forecasting model for electricity consumption, Jet Time Series Forecaster performs a stationarity analysis. This helps in determining if the data needs differencing or transformation, ensuring the model's accuracy and reliability.

Example

Building and optimizing SARIMA models to forecast future values based on historical data.

Scenario

For a company looking to predict quarterly revenue, Jet Time Series Forecaster iteratively tests various SARIMA configurations. It identifies the best model based on criteria like AIC and SBC values, enabling precise revenue forecasts and better financial planning.

Example

Calculating prediction errors using metrics like MAE and RMSE to evaluate the model's performance.

Scenario

After developing a model to forecast daily website traffic, Jet Time Series Forecaster calculates error metrics on a validation dataset. This allows the user to assess the model's accuracy and adjust their digital marketing strategies accordingly.

Example

Visualizing actual vs. predicted values to assess model performance visually.

Scenario

Jet Time Series Forecaster uses the optimal SARIMA model to predict stock levels needed for the upcoming holiday season. It then visually compares these predictions against actual stock levels from previous years, aiding in inventory management.