Can Statsmodels Handle Time-Series Data in Catalysis?
Yes, Statsmodels is highly capable of handling time-series data, which is often encountered in catalysis research. Researchers may need to analyze the time-dependent behavior of catalytic reactions, such as the deactivation of a catalyst over time. Statsmodels provides tools for time-series analysis including ARIMA (AutoRegressive Integrated Moving Average) models, state space models, and more. Here is an example of fitting an ARIMA model:
from statsmodels.tsa.arima.model import ARIMA
# Assume 'time_series_data' is a pandas Series containing your time-series data model = ARIMA(time_series_data, order=(1, 1, 1)) fit_model = model.fit()
