Moving averages involve calculating the average of a series of past data points and using this average as a forecast for the next period.

Exponential smoothing assumes that the rate of change in the data is constant, allowing for a smooth transition between past and future values.

In regression analysis, the dependent variable is the variable whose value is being predicted based on the values of one or more independent variables.

ARIMA models are designed to account for autocorrelation, or the correlation between successive data points, in time series data.

Seasonal Decomposition of Time Series is a technique specifically designed to handle seasonality by decomposing the data into trend, seasonal, and residual components.

The Box-Jenkins methodology is a step-by-step procedure for identifying, estimating, and validating ARIMA models.

Moving averages are often used for short-term predictions due to their simplicity and ability to capture recent trends in the data.

The primary goal of forecast evaluation is to assess the accuracy of the forecast by comparing the predicted values with the actual outcomes.

Root Mean Squared Error (RMSE) is a widely used measure for evaluating the accuracy of point forecasts, as it penalizes large errors more heavily than small errors.

Combining multiple forecasting techniques can lead to improved accuracy and robustness, as it reduces the reliance on a single method and takes advantage of the strengths of different techniques.

Differencing is a technique used to transform non-stationary time series data into stationary data, making it more suitable for forecasting using techniques like moving averages or exponential smoothing.

Forecast horizons are used to specify the length of the period for which forecasts are being made, ranging from short-term (e.g., daily or weekly) to long-term (e.g., monthly or yearly).

Linear Regression is often used when the data exhibits a linear trend, allowing for the prediction of future values based on the historical relationship between the dependent and independent variables.

A rolling forecast approach involves updating the forecast periodically with new data, leading to improved accuracy and robustness, as it adapts to changing conditions and trends.

Seasonal Decomposition of Time Series is specifically designed to handle data with cyclical patterns, allowing for the separation of the trend, seasonal, and residual components of the data.