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Power and Pitfalls of the Autoregressive Integrated Moving Average (ARIMA)
Model in Time Series Forecasting
In the domain of time series analysis, the Autoregressive Integrated Moving Average (ARIMA) model stands as a workhorse for making forecasts. It is an extension of the Autoregressive Moving Average (ARMA) model and incorporates the idea of integration to make the model applicable to non-stationary data. Although popular, ARIMA is sometimes misunderstood. This article aims to demystify the ARIMA model, explain its components, and discuss its advantages and limitations.
Basics of Time Series Analysis
Before exploring the intricacies of the ARIMA model, one must have a firm grasp of the fundamentals of time series analysis. Time series data is a sequence of observations collected or recorded at a constant time interval. This could be anything from the daily temperature of a city to the yearly revenue of a company. The objective of analyzing time series data is multi-faceted and involves several key aspects:
Components of a Time Series
- Trend: This is the general direction in which the data is moving over a long period. A trend could be upward, downward, or even horizontal.
- Seasonality: These are patterns…
