Time Series Forecasting is important in businesses to predict future outcomes, assess past performances, and identify patterns and trends in metrics. It can also provide insights into variables that change over time, such as stock prices, sales figures, and customer behavior and other time-dependent variables.

Forecasting methods using time series are used in both fundamental and technical analysis. A time series can be taken on any variable that change over time. Some techniques used for forecasting include moving averages, exponential smoothing, and ARIMA models. Time series analysis can also help identify characteristics of a time series, such as: Seasonality, Cyclical, Trend and Autocorrelation. Eleven different classical time series forecasting methods will be taught in detail.

Towards the end of the course, end to end project named Stock Price Forecasting using Time Series Forecasting with Python will be covered. This course will equip you as experts in Time Series Forecasting.

This course is open to both engineering and arts college students. And is suitable for working professionals as well who wish to have their domain change. This course is for everyone being fresh graduates, pre final year trying for placements (both computer and non computer engineering / arts), UG, PG to be graduates, working professionals seeking for domain change/ new offer with nice package and for professors from any department and research scholars, and to all UG, PG students who wish to do final semester project using Python. 5+ End to End projects will be taught and new challenges will be given based on participants wish to make them try on their own.