Performed time series analysis on ‘economic indicators’ data c to understand underlying patterns such as trends, seasonality, and cyclic behaviours.
The data was organised chronologically by time variable (‘Year’ and ‘Month’ columns).
Later, to indicate time, I combined the ‘Year’ and ‘Month’ columns into a single date time column.
Also visualised the time series data using time series plots to observe patterns, trends, and seasonality in each economic indicator over time.
Then, to analyse the specific contributions, I decomposed the time series into its components (trend, seasonality, residual) using techniques such as seasonal decomposition.
Seasonal decomposition is a time series analysis approach that divides a time series dataset into three components: trend, seasonality, and residual or error components.
Applied forecasting model like ARIMA (AutoRegressive Integrated Moving Average) to predict future values of economic indicators.
Then I split the data into training and test sets to train the model and evaluate its performance.
used the metric like Mean Absolute Error (MAE) to assess the accuracy of the forecasting model and compared the predicted values against the actual values in the test dataset to evaluate the model’s performance.