What are the Different Types of Time Series Forecasting Methods in 2024

Cybrosys Technologies 21-02-2024

1. Auto Regression

Auto-regression stands out as a time series analysis technique that forecasts upcoming values by establishing a linear relationship with its preceding observations. Time Series Forecasting M

2. Moving Averages

Moving averages, an essential tool in time series analysis, mitigate fluctuations and unveil trends by computing the average of successive data points across a specified period. Time Series Forecasting M

3. ARIMA

ARIMA stands out as a potent time series forecasting technique that integrates autoregression, differencing, & moving averages to make accurate predictions. Time Series Forecasting M

4. SARIMA

SARIMA represents an extension of the ARIMA model specifically crafted to address time series data characterized by seasonal patterns. Time Series Forecasting M

5. Prophet

Prophet, a forecasting tool created by Facebook for time series analysis and prediction, is tailored to manage datasets with daily observations. Time Series Forecasting M

6. Exponential Smoothing

Exponential smoothing stands as a time series forecasting method that allocates varying weights to past observations, with greater emphasis on more recent data. Time Series Forecasting M