Welcome to Complete Practical Time Series Analysis and Forecasting in Python
Time series analysis and forecasting is one of the areas of Data Science and has a wide variety of applications in the industries in the current world. Many industries looking for a Data Scientist with these skills. This course covers all type of modeling techniques for forecasting and analysis.
We start with programming in Python which is the essential skill required and then we will exploring the fundamental time series theory to help you understand the modeling that comes afterward.
Then throughout the course, we will work with a number of Python libraries, providing you with complete training. We will use the powerful time-series functionality built into pandas, as well as other fundamental libraries such as NumPy, matplotlib, statsmodels, Sklearn, and ARCH.
With these tools we will master the most widely used models out there:
- Additive Model
- Multiplicative Model
· AR (autoregressive model)
· Simple Moving Average
- Weighted Moving Average
- Exponential Moving Average
· ARMA (autoregressive-moving-average model)
· ARIMA (autoregressive integrated moving average model)
. SARIMA (seasonal autoregressive integrated moving average model)
. SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)
· ARCH (autoregressive conditional heteroscedasticity model)
· GARCH (generalized autoregressive conditional heteroscedasticity model)
We know that time series is one of those topics that always leaves some doubts.
This course is exactly what you need to comprehend the time series once and for all. Not only that, but you will also get a ton of additional materials – notebooks files, course notes – everything is included.