In this project we are predicting google stock for next 10 days, evaluating which model (ARIMA or SARIMA) gives more accurate results.

• Collecting (Extract) Google stock price data using the Yahoo Finance API. (yfinance is an API provided by Yahoo Finance to collect the latest stock price data.)
• For the sake of simplicity, I’ve limited the data to past 360 days (2023-07-20 to 2022-07-20)
• Transforming & Plotting the time series data.
• Figuring out whether our data is stationary or seasonal using Seasonal Decomposition Method.
• Plotting ACF and PACF plots to get an idea for p and q values.
• Building ARIMA Model & plotting the results.
• Building SARIMA Model & plotting the results.

• Data is seasonal.
• A 6-month and 12-month seasonal pattern is visible.
• SARIMA gave more accurate results as compared to ARIMA, as data is Seasonal

• Analyzing and modeling time-series data to make future decisions.
• Applications : weather forecasting, sales forecasting, business forecasting, stock price forecasting

• ARIMA means Autoregressive Integrated Moving Average.
• ARIMA models have three parameters like ARIMA(p, d, q)
  • Autoregressive Part (p) : It is number of lagged values that need to be added or subtracted from the values (label column).
  • Integrated Part (d) - It respresents number of times the data needs to differentiate to produce a stationary signal.
    • Stationary Data : d = 0;
    • Seasonal Data : d = 1;
  • Moving Average (q) - It is number of lagged values for the error term added or subtracted from the values (label column).

• SARIMA means Seasonal ARIMA.
• With ARIMA parameters, In this, we include seasonality variable as well.
• There is an additional set of autoregressive and moving average components.The additional lags are offset by the frequency of seasonality. (12 months, 6 months etc. depending on the dataset.)
• SARIMA models not only allow for differencing data by seasonal frequency, but also by non-seasonal differencing.

• SARIMA means Seasonal ARIMA.
• With SARIMA parameters, In this, we include exogenous variables as well. ( we use external data in our forecast)
• Some Real-world examples of exogenous variables are gold price, oil price, outdoor temperature, exchange rate.
• If we include external data, the model will respond much quicker to its affect than if we rely on the influence of lagging terms.