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

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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.

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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.

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

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

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5. Prophet

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

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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.

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