Time Series Quick Reference Guide

Quick reference for time series analysis techniques.

Table of Contents

• Key Concepts
• Code Snippets
• Model Selection
• Common Issues & Solutions
• Best Practices Checklist

Key Concepts

Stationarity

from statsmodels.tsa.stattools import adfuller
result = adfuller(ts)
is_stati>1] <= 0.05

Time-based Split

split_idx = int(len(df) * 0.8)
train = df.iloc[:split_idx]
test = df.iloc[split_idx:]

Code Snippets

ARIMA

from statsmodels.tsa.arima.model import ARIMA
model = ARIMA(ts, order=(2, 1, 2))
fitted = model.fit()
forecast = fitted.forecast(steps=10)

Auto ARIMA

from pmdarima import auto_arima
model = auto_arima(ts, seasonal=True, m=12)

LSTM

model = Sequential([
    LSTM(50, input_shape=(seq_length, 1)),
    Dense(1)
])

Model Selection

Common Issues & Solutions

Issue 1: Non-stationary Data

Solution: Differencing, log transform

Issue 2: Data Leakage

Solution: Always use time-based split

Best Practices Checklist

• Use time-based train/test split
• Check stationarity
• Decompose time series
• Try multiple models
• Use proper metrics (RMSE, MAE)
• Visualize predictions

Try next: Split by time, not by random rows, on your next time-series notebook.