Hamid Muili
There are series of forecasting techniques, u can use ARIMA, ETS, artificial neural network, random forest etc. It all depends on the nature of your data.
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Chuck A Arize
Common forecasting techniques in time series analysis include ARIMA (AutoRegressive Integrated Moving Average), Exponential Smoothing (e.g., Holt-Winters), Vector Autoregression (VAR), Seasonal Decomposition, and Machine Learning methods like Random Forests and Neural Networks.
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Chuck A Arize
ARIMA (AutoRegressive Integrated Moving Average): Combines autoregressive (AR) models, moving average (MA), and differencing to make the series stationary. It’s widely used for short-term forecasting. Exponential Smoothing (e.g., Holt-Winters): This method weights past observations exponentially, with more recent data given more weight. It’s useful for series with trends and seasonality. Seasonal Decomposition of Time Series (STL): Breaks down a time series into trend, seasonal, and residual components, helping to understand the underlying patterns. Vector Autoregression (VAR): A multivariate model used for forecasting multiple time series simultaneously, useful when variables influence each other. Machine Learning Techniques: Methods like Random Forests, Support Vector Machines, and Neural Networks can be used for non-linear time series forecasting, particularly when there are complex patterns beyond simple statistical models.
Here’s a brief explanation of common forecasting techniques used in time series analysis:
These methods can be applied based on the nature of the data and the forecasting horizon.
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