We basically discussed the type of Dataset we should work on for our project along with that also discussed various Time Series Algorithms.
Time series are broken down into their component parts using conventional techniques like Autoregressive Integrated Moving Average (ARIMA) and Seasonal-Trend decomposition using LOESS (STL). ETS models, or exponential smoothing state space models, account for seasonality, trend, and error. Complex dependencies are handled by sophisticated machine learning models such as XGBoost, Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM). Seasonality-based forecasting is the area of expertise for algorithms like Prophet and SARIMA. Time series data trends and seasonality are accommodated by Holt-Winters exponential smoothing.