Modeling
Simple Exponential Smoothing
Simple Exponential Smoothing Description It is a widely used technique in Time-Series forecasting. It predicts the future values of a time series based on historical data. Why to Use To forecast future interval values. When to Use On a stable dataset ...
SARIMA
SARIMA Description SARIMA is an abbreviation for Seasonal Autoregressive Integrated Moving Average. It is an extension of ARIMA used to model seasonal time series. SARIMA considers the events occurring at regular intervals and impacting the target ...
Moving Average in Forecasting
Moving Average in Forecasting Description The Moving Average is also known as Naïve Forecasting or moving/rolling mean. It is an indicator that creates a series of averages of several subsets of a complete dataset Why to use The Moving Average is ...
LSTM
LSTM (Long Short-Term Memory) Description Forecasting and Prediction of the model include numerical and date dataset. Why to use Forecasting Time series When to use To classify, process, and make predictions based on time series data. When not to use ...
Holts-Winters Exponential Smoothing
Holts-Winters Exponential Smoothing Description The Holt-Winters Exponential Smoothing, also known as Triple Exponential Smoothing. It is a powerful time series forecasting technique that extends simple exponential smoothing by adding support for ...
Holt Exponential Smoothing
Holt Exponential Smoothing Description Holt Exponential Smoothing, also known as Double Exponential Smoothing, is a forecasting. It is an extension of Simple Exponential Smoothing. This method is used for data that exhibit a trend but no seasonal ...
Auto ARIMA
Auto ARIMA Description Auto ARIMA (Auto-Regressive Integrated Moving Average) is a statistical algorithm that uses time series data to forecast future values. It automatically determines the best parameters for an ARIMA model. Why to use Auto ARIMA ...
ARIMA
ARIMA Description ARIMA stands for Auto-Regressive Integrated Moving Average. It is used for forecasting on stationary data. Why to use ARIMA captures both autoregressive and moving average components, which provides a systematic way to deal with ...
Modeling
Models for time series data can have many forms and represent different stochastic. When modeling variations in the level of a process, three broad classes of practical importance are the AutoRegressive (AR) models, the Integrated (I) models, and the ...