Time Series Analysis

Time Series Analysis deals with data collected over time - like daily stock prices or monthly sales - where the order of observations carries meaningful information.

Key Components of a Time Series

Trend:        The long-term increase or decrease in the data
Seasonality:  Regular, repeating patterns over a fixed period (e.g. daily, yearly)
Noise:        Random variation that isn't explained by trend or seasonality

Visualizing a Time Series in Python

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    "Month": pd.date_range(start="2026-01-01", periods=12, freq="M"),
    "Sales": [200, 220, 210, 250, 270, 300, 320, 310, 330, 360, 400, 420]
})

df.set_index("Month", inplace=True)
df["Sales"].plot(title="Monthly Sales")
plt.show()

A Simple Forecasting Approach

from statsmodels.tsa.holtwinters import ExponentialSmoothing

model = ExponentialSmoothing(df["Sales"], trend="add", seasonal=None)
fit = model.fit()

forecast = fit.forecast(3)
print(forecast)

Why Time Series Needs Special Handling

Standard machine learning models assume observations are independent, but time series data is inherently sequential - values are often correlated with their own past values, which regular models don't account for.

Before modeling, it's common to check whether a time series is "stationary" (constant mean and variance over time), since many forecasting methods assume stationarity.

Coming Up Next