Unemployment Rate Forecasting
1. Introduction
Objective: Predict the unemployment rate using historical data and advanced
time series forecasting models such as ARIMA or Prophet.
Purpose: Provide insights into labor market trends to assist policymakers and
stakeholders in decision-making.
2. Project Workflow
1. Problem Definition:
- Forecast future unemployment rates
based on historical trends.
- Key questions:
- What are the trends in
unemployment rates over time?
- How accurately can future
unemployment rates be predicted?
2. Data Collection:
- Source: Government databases (e.g.,
Bureau of Labor Statistics), Kaggle datasets.
3. Data Preprocessing:
- Clean, transform, and structure the
time series data.
4. Exploratory Data Analysis:
- Analyze trends, seasonality, and
patterns in the data.
5. Modeling:
- Apply ARIMA and Prophet for
forecasting.
6. Evaluation and Insights:
- Evaluate the performance of models
and provide actionable insights.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn,
Plotly
- Time Series Modeling: statsmodels,
fbprophet, scikit-learn
4. Implementation Steps
Step 1: Setup Environment
Install required libraries:
```
pip install pandas numpy matplotlib seaborn statsmodels prophet scikit-learn
```
Step 2: Collect and Explore Data
Load unemployment rate data:
```
import pandas as pd
data = pd.read_csv("unemployment_rate.csv", parse_dates=['Date'],
index_col='Date')
print(data.head())
```
Inspect the dataset:
```
print(data.info())
print(data.describe())
```
Step 3: Visualize the Data
Plot the unemployment rate over time:
```
import matplotlib.pyplot as plt
plt.plot(data['Unemployment Rate'])
plt.title('Unemployment Rate Over Time')
plt.xlabel('Date')
plt.ylabel('Unemployment Rate')
plt.show()
```
Step 4: Forecast with ARIMA
Fit an ARIMA model to the data:
```
from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(data['Unemployment Rate'], order=(1, 1, 1))
arima_result = model.fit()
print(arima_result.summary())
```
Forecast future values:
```
forecast = arima_result.forecast(steps=12)
print(forecast)
```
Step 5: Forecast with Prophet
Prepare data for Prophet:
```
from prophet import Prophet
prophet_data = data.reset_index().rename(columns={'Date': 'ds', 'Unemployment
Rate': 'y'})
model = Prophet()
model.fit(prophet_data)
```
Make predictions:
```
future = model.make_future_dataframe(periods=12, freq='M')
forecast = model.predict(future)
model.plot(forecast)
plt.show()
```
Step 6: Evaluate Models
Evaluate model performance using metrics like Mean Absolute Error (MAE):
```
from sklearn.metrics import mean_absolute_error
arima_mae = mean_absolute_error(data['Unemployment Rate'][-12:], forecast[:12])
print(f'ARIMA MAE: {arima_mae}')
```
5. Expected Outcomes
1. Visualizations of historical unemployment rate trends.
2. ARIMA and Prophet-based forecasts of future unemployment rates.
3. Insights into labor market dynamics and patterns.
6. Additional Suggestions
- Experiment with different ARIMA parameters (p, d, q) for optimization.
- Include external regressors (e.g., GDP, inflation rate) for more accurate
forecasts.
- Build a dashboard to visualize historical data and real-time forecasts.