Bike Sharing Demand Forecasting

 Bike Sharing Demand Forecasting 

1. Introduction


Objective: Develop a time series forecasting model to predict bike-sharing demand based on historical data.
Purpose: Assist bike-sharing companies in optimizing resource allocation and meeting user demand efficiently.

2. Project Workflow


1. Problem Definition:
   - Predict future bike-sharing demand using time series data.
   - Key questions:
     - What are the patterns in bike-sharing demand?
     - How can we accurately forecast future demand?
2. Data Collection:
   - Source: Bike-sharing datasets from public repositories.
   - Example fields: Date/Time, Temperature, Weather, Holiday, Demand.
3. Data Preprocessing:
   - Handle missing values, outliers, and transform date/time features.
4. Model Development:
   - Use time series forecasting techniques such as ARIMA, SARIMA, or LSTM.
5. Model Evaluation:
   - Evaluate model performance using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

3. Technical Requirements


- Programming Language: Python
- Libraries/Tools:
  - Data Handling: Pandas, NumPy
  - Data Visualization: Matplotlib, Seaborn
  - Time Series Analysis: Statsmodels, Scikit-learn, TensorFlow (for advanced models)

4. Implementation Steps

Step 1: Setup Environment


Install required libraries:
```
pip install pandas numpy matplotlib seaborn statsmodels tensorflow
```

Step 2: Load and Explore Data


Load the bike-sharing dataset:
```
import pandas as pd

data = pd.read_csv("bike_sharing_data.csv")
print(data.head())
```
Explore key statistics and visualize trends:
```
import matplotlib.pyplot as plt

data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)

plt.figure(figsize=(12, 6))
plt.plot(data['Demand'], label='Bike Demand')
plt.xlabel('Date')
plt.ylabel('Demand')
plt.title('Bike Sharing Demand Over Time')
plt.legend()
plt.show()
```

Step 3: Preprocess Data


Handle missing values and extract time-based features:
```
data = data.fillna(method='ffill')  # Forward fill missing values

# Extract features
data['Year'] = data.index.year
data['Month'] = data.index.month
data['Day'] = data.index.day
data['DayOfWeek'] = data.index.dayofweek
```
Perform seasonal decomposition:
```
from statsmodels.tsa.seasonal import seasonal_decompose

decomposition = seasonal_decompose(data['Demand'], model='additive', period=365)
decomposition.plot()
plt.show()
```

Step 4: Build Time Series Model


Split data into training and testing sets:
```
train = data[:'2021']
test = data['2022':]

train_demand = train['Demand']
test_demand = test['Demand']
```
Train an ARIMA model:
```
from statsmodels.tsa.arima.model import ARIMA

model = ARIMA(train_demand, order=(5, 1, 0))
model_fit = model.fit()
print(model_fit.summary())
```
Predict on the test set:
```
forecast = model_fit.forecast(steps=len(test))
plt.plot(train_demand, label='Train')
plt.plot(test_demand, label='Test')
plt.plot(forecast, label='Forecast')
plt.legend()
plt.show()
```

Step 5: Evaluate Model


Evaluate the model using MAE and RMSE:
```
from sklearn.metrics import mean_absolute_error, mean_squared_error

mae = mean_absolute_error(test_demand, forecast)
rmse = mean_squared_error(test_demand, forecast, squared=False)

print("MAE:", mae)
print("RMSE:", rmse)
```

Step 6: Advanced Modeling (Optional)


Experiment with advanced time series techniques like LSTMs:
```
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# Build and train an LSTM model
```

5. Expected Outcomes


1. A trained time series model capable of forecasting bike-sharing demand.
2. Insights into seasonal and temporal patterns affecting demand.
3. Model evaluation metrics for assessing forecast accuracy.

6. Additional Suggestions


- Deployment:
  - Develop a dashboard to visualize historical and predicted demand.
  - Use tools like Flask or Streamlit for deployment.
- Feature Engineering:
  - Incorporate additional features like weather conditions and holidays.
- Continuous Learning:
  - Update the model with new data periodically for better accuracy.