Predict the future with data.

What is Time Series?

Data collected over time: stock prices, temperature, sales.

**Goal**: Predict future values

Key Concepts

**Trend**: Long-term direction (up/down) **Seasonality**: Repeating patterns (summer/winter) **Noise**: Random fluctuations

Simple Moving Average

```python import pandas as pd

Calculate 7-day moving average df['MA_7'] = df['sales'].rolling(window=7).mean()

Predict: next value = average of last 7 ```

ARIMA Model

Classic time series model:

```python from statsmodels.tsa.arima.model import ARIMA

Fit ARIMA model = ARIMA(data, order=(1, 1, 1)) model_fit = model.fit()

Forecast next 10 days forecast = model_fit.forecast(steps=10) print(forecast) ```

LSTM for Time Series

```python from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense import numpy as np

Prepare data: use last 30 days to predict next day def create_sequences(data, seq_length=30): X, y = [], [] for i in range(len(data) - seq_length): X.append(data[i:i+seq_length]) y.append(data[i+seq_length]) return np.array(X), np.array(y)

X, y = create_sequences(prices)

Build model model = Sequential([ LSTM(50, return_sequences=True, input_shape=(30, 1)), LSTM(50), Dense(1) ])

model.compile(optimizer='adam', loss='mse') model.fit(X, y, epochs=50, batch_size=32)

Predict next_day = model.predict(last_30_days) ```

Prophet (by Facebook)

Easy and powerful:

```python from fbprophet import Prophet

Data needs 'ds' (date) and 'y' (value) df = pd.DataFrame({ 'ds': dates, 'y': values })

model = Prophet() model.fit(df)

Forecast 365 days future = model.make_future_dataframe(periods=365) forecast = model.predict(future)

Plot model.plot(forecast) ```

Evaluation Metrics

```python from sklearn.metrics import mean_absolute_error, mean_squared_error

mae = mean_absolute_error(y_true, y_pred) rmse = np.sqrt(mean_squared_error(y_true, y_pred))

print(f"MAE: {mae}") print(f"RMSE: {rmse}") ```

Real Applications

- Stock price prediction - Sales forecasting - Weather prediction - Traffic forecasting - Energy consumption

Best Practices

1. Check for stationarity 2. Handle seasonality 3. Use walk-forward validation 4. Don't predict too far ahead

Remember

- Time order matters! - Don't shuffle data - Use Prophet for easy start - LSTM for complex patterns