Hii,
In todays analysis, that I learned about the time series models for predecting the future data.
- SARIMA (Seasonal Autoregressive Integrated Moving Average): This model extends the ARIMA model by incorporating seasonality. SARIMA is useful in datasets where seasonal patterns are prominent, and it includes additional seasonal parameters to account for these patterns.
- VAR (Vector Autoregression): The VAR model is used for multivariate time series data, where the system captures the linear interdependencies among multiple variables. It’s particularly useful when you want to model and predict systems where variables influence each other.
- LSTM (Long Short-Term Memory): LSTM is a type of recurrent neural network (RNN) particularly effective in learning order dependence in sequence prediction problems. This model is well-suited for time series data where there are long-term dependencies or patterns.
- ARIMA (Autoregressive Integrated Moving Average): ARIMA is one of the classic models used for time series forecasting. It combines autoregressive features with moving averages and integrates differencing to make the time series stationary, making it effective for a wide range of datasets.