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.