Time series analysis was done on the “economic indicators” data to identify underlying trends, seasonality, and cyclical behaviors.
By time variable, the data were arranged chronologically in the “Year” and “Month” columns.
I later created a single date time column by combining the ‘Year’ and ‘Month’ columns to show the time.
Additionally, time series plots were used to visualize the data in order to see trends, patterns, and seasonality in each economic indicator over time.
Then I used methods like seasonal decomposition to break down the time series into its constituent parts (trend, seasonality, and residual) in order to analyze the individual contributions.
A time series dataset is split into three parts using the seasonal decomposition technique: the trend, seasonality, and residual or error components.
Used forecasting models to project future values of economic indicators, such as ARIMA (AutoRegressive Integrated Moving Average). After that, I divided the data into test and training sets so that I could train the model and assess its effectiveness.
Evaluated the forecasting model’s accuracy using a metric such as Mean Absolute Error (MAE), and evaluated the model’s performance by comparing the predicted values with the test dataset’s actual values.
