Handle Missing Timestamps

Missing data in time series creates uneven intervals, which breaks most forecasting models that assume regular spacing.

The Problem

Real-world time series often have gaps — missing days, weekends, or sensor outages. Models like ARIMA and Prophet expect evenly spaced observations.

Strategy

1. Resample to the expected frequency to create a complete date index.
2. Fill the gaps using an appropriate method.

Implementation

import pandas as pd
import numpy as np

# Create a time series with missing dates
dates = pd.to_datetime(["2024-01-01", "2024-01-02", "2024-01-05",
                         "2024-01-06", "2024-01-09", "2024-01-10"])
values = [100, 102, 108, 110, 115, 117]
ts = pd.Series(values, index=dates)

print("Before — gaps present:")
print(ts)

# 1. Resample to daily frequency (creates NaN for missing days)
ts_resampled = ts.resample("D").asfreq()

# 2. Forward fill — carry the last known value forward
ts_ffill = ts_resampled.ffill()

# OR: Linear interpolation — estimate between known points
ts_interp = ts_resampled.interpolate(method="linear")

print("\nForward Fill:")
print(ts_ffill)

print("\nLinear Interpolation:")
print(ts_interp)

Choosing a Fill Method

Method	When to Use
ffill()	Sensor data where the last reading carries forward (e.g., temperature)
bfill()	When you expect the next value to be more representative
interpolate(method='linear')	When a smooth transition between points is reasonable
interpolate(method='time')	When gaps are irregular and you want time-weighted interpolation

Common Pitfall

Do not forward-fill over very long gaps. If a sensor was offline for a week, carrying the last reading forward for 7 days introduces misleading flat segments. Consider dropping those periods or flagging them instead.

KSB Mapping

KSB	Description	How This Addresses It
K4.1	Statistical models and methods	ARIMA, SARIMA, and exponential smoothing foundations
K4.2	Predictive analytics and ML techniques	Time series forecasting and model comparison
K5.3	Common patterns in real-world data	Identifying trends, seasonality, and stationarity
S1	Scientific methods and hypothesis testing	Stationarity testing, model diagnostics, forecast validation
S4	Analysis and models to inform outcomes	Building forecasts to support business planning
B5	Impartial, hypothesis-driven approach	Honest evaluation of forecast accuracy and limitations