Data Science · Chapter 35 of 43
Time Series Basics
TIME SERIES data is indexed by time (daily sales, hourly temperature). Order matters — splits must be chronological.
Common tasks: forecasting, anomaly detection, trend/seasonality decomposition.
Example 1 (python)
import pandas as pd
df = pd.read_csv('sales.csv', parse_dates=['date'], index_col='date')
print(df.resample('M').sum().head())Monthly totals.
Example 2 (python)
df['rolling_7'] = df['sales'].rolling(7).mean()7-day moving average.
Key points
- Order matters — split chronologically.
- Common patterns: trend + seasonality.
- Resampling changes frequency.
- Rolling stats smooth noise.
💡 Note: Never randomly shuffle time-series data — you'll leak the future into the past and get unrealistically great scores.