Time series forecasting with ARMA

 

Time series forecasting with ARMA

An ARMA or autoregressive moving average model is a forecasting model that predicts future values based on past values. Forecasting is a critical task for several business objectives, such as predictive analytics, predictive maintenance, product planning, budgeting, etc. A big advantage of ARMA models is that they are relatively simple. They only require a small dataset to make a prediction, they are highly accurate for short forecasts, and they work on data without a trend.

 

What is ARMA?

ARMA stands for the auto-regressive moving average. It’s a forecasting technique that is a combination of AR (auto-regressive) models and MA (moving average) models. An AR forecast is a linear additive model. The forecasts are the sum of past values times a scaling factor plus the residuals.

The autoregressive (AR) component of an ARMA model predicts the future value of a time series based on its past values. It assumes that the current value of the series is linearly dependent on its previous values. The "autoregressive" part refers to the fact that it uses a linear regression of the time series against its own lagged values.

The moving average (MA) component of an ARMA model models the errors or residuals of the time series. It assumes that the errors are linearly dependent on the error terms from previous time steps. The "moving average" part refers to the fact that it uses a linear combination of the error terms.

 

Conclusion:

By combining autoregressive and moving average components, the ARMA model can capture both linear dependences on past observations and linear dependence on past errors. It gets used in a variety of disciplines, such as engineering, finance, and economics, where it is essential to recognize and forecast patterns in time-dependent data.

 

Reference:

1 .https://www.infoworld.com/article/3681894/time-series-forecasting-with-arma-and-influxdb.html

2. https://chat.openai.com/

Aniket Shukla

ISME Student Doing an internship with Hunnarvi under the guidance of nanobi data and analytics. Views are personal.

# Time series forecasting with ARMA # analytics #nanobi #hunnarvi #ISME

 

 

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