Time Series Analysis allows us to analyze data that is generated over a period of time and has sequential interdependencies between the observations. This video describes special mathematical tricks and techniques that are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the tutorial is full of real-life time series examples and their analyses using cutting-edge solutions developed in Python. The video starts with a descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality, and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift the focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented to develop accurate forecasting models for complex time series. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python.
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