Data Science — Time Series

Time series components

1. Trend: Long-term direction
2. Seasonality: Regular patterns
3. Noise: Random variation

Basic analysis

import pandas as pd
import matplotlib.pyplot as plt

# Load time series
df = pd.read_csv('data.csv', parse_dates=['date'], index_col='date')

# Plot
df['value'].plot(figsize=(10, 6))
plt.show()

Rolling statistics

# Moving average
df['ma_7'] = df['value'].rolling(window=7).mean()
df['ma_30'] = df['value'].rolling(window=30).mean()

ARIMA

from statsmodels.tsa.arima.model import ARIMA

model = ARIMA(df['value'], order=(1, 1, 1))
fitted = model.fit()
forecast = fitted.forecast(steps=30)

Prophet

from prophet import Prophet

df_prophet = df.reset_index()
df_prophet.columns = ['ds', 'y']

model = Prophet()
model.fit(df_prophet)

future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)

Mini Practice

1. Analyze trends
2. Detect seasonality
3. Build ARIMA model
4. Use Prophet

Up Next

Continue with NLP - Natural Language Processing.