Time Series Forecasting using SARIMA
Budget: $10 – $30 USD
Perform (in Python) univariate time series forecasting of monthly data based on previous few years of data using SARIMA algorithm.
Before applying the algorithm:
• If needed, remove variance using the Box-Cox method.
• Check stationarity using the augmented Dickey-Fuller test.
• Use differencing to make the dataset stationary.
• Print ACF/PACF to select the parameters of the SARIMA model.
Forecast next six months, calculate R Squared and RMSE.
Before applying the algorithm:
• If needed, remove variance using the Box-Cox method.
• Check stationarity using the augmented Dickey-Fuller test.
• Use differencing to make the dataset stationary.
• Print ACF/PACF to select the parameters of the SARIMA model.
Forecast next six months, calculate R Squared and RMSE.
Related categories:
Python