ARIMA for Time series data

ARIMA (Autoregressive Integrated Moving Average):

In an autoregressive model, the forecast of a variable is based on its linear combination of past values of the same variable.

  1. Autoregressive (AR):  ARIMA looks at the relationship between the current value of a time series and its past value. It considers how the past values of a variable contribute to predicting its future values.
    • If yesterday’s stock prices influenced today’s, ARIMA captures this influence.
  2. Integrated (I): It aims to make the time series data stationary, meaning its statistical properties like mean and variance remain constant over time. It checks if the data has a trend or changing statistical properties.
    • If the data is not stationary, ARIMA applies differencing to make it more predictable.
  3. Moving Average (MA): It involves considering the past forecast errors to predict future values. Instead of looking at past values of the variable, ARIMA looks at past errors in predicting the variable.
    • It considers how the errors from previous predictions influence the current prediction.

Understanding ARIMA in Simple Steps:

  1. Start with the Data:
    • Data should be more like having a series of numbers, like daily stock prices or monthly website visits—something that changes over time.
  2. Understand Trends:
    • ARIMA looks at the overall trend in the data. It analysis if the trend is generally going up or down. This helps to understand the baseline behavior.
  3. Deal with Trends:
    • If there’s a trend, ARIMA helps to remove it by differencing.
  4. Autoregressive Part:
    • It checks how today’s data relates to the data from the past. If there’s a clear connection, it uses this to make predictions.
  5. Moving Average Part:
    • ARIMA considers how accurate past predictions were and adjusts for any mistakes made. It’s a way of learning from past experiences.
  6. Combine Everything for Predictions:
    • The algorithm combines the insights from trends, historical data, and past predictions to make an informed guess about what might happen next

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