A Practical Approach to Timeseries Forecasting Using Python - Stages for Time Series Forecasting

The video tutorial covers the stages of time series analysis and forecasting, starting with data manipulation using libraries like pandas to format data for time series use. It progresses to data processing, where the behavior of data is analyzed, and data visualization, which involves creating graphs to assess data quality. The tutorial then explains the computation of time series parameters and feature checking, followed by ensuring data stationarity. It discusses model building using algorithms like ARIMA and RNN for forecasting, and concludes with performance evaluation of machine learning and deep learning models.

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