Summary
In this chapter, we delved into the fascinating world of time series analysis. We began by exploring time series plotting, mastering essential plots, and understanding the significance of ACF/PACF plots.
Moving forward, we ventured into time series statistics, including the ADF test, time series decomposition, and statistical forecasting with tools such as statsmodels and prophet.
To elevate our forecasting game, we embraced deep learning, employing LSTM networks using Python’s keras library. We learned to develop accurate time series forecasts and create insightful visualizations for data-driven insights.
This chapter equipped us with a comprehensive set of skills for time series analysis, enabling us to unravel the hidden patterns and insights within time-based data, from plotting to statistical analysis and deep learning forecasting.
In the next chapter, we will discuss a different integration method – that is, calling R and Python from Excel directly...
