Forecasting Techniques: From Moving Averages to ARIMA

Forecasting isn’t just a buzzword—it’s the secret sauce behind business decisions, stock market moves, and demand planning. Whether you’re a data analyst, student, or enthusiast, mastering forecasting techniques like Moving Averages and ARIMA can transform how you look at numbers.

🚀 Why Forecasting Matters

Forecasting answers the question: “What’s likely to happen next?” From predicting next month's sales to future temperature trends or web traffic, it plays a critical role in every data-driven domain.

📊 1. Simple Moving Average (SMA)

What it is: The average of a fixed number of previous data points. Formula:

SMA_t = (X_t + X_{t-1} + … + X_{t-n+1}) / n

Use Cases:

• Stock price trends
• Smooth out short-term fluctuations

Pros: Easy to understand Cons: Lags behind actual trends

📈 2. Exponential Moving Average (EMA)

What’s new: EMA gives more weight to recent data compared to SMA. Ideal for capturing short-term movements while reducing lag.

Why Use It?

• Reacts faster to recent changes
• Commonly used in technical trading indicators

In Python:

df['EMA_10'] = df['value'].ewm(span=10, adjust=False).mean()

📉 3. Linear Regression Forecasting

Think trendlines. Fit a line to past data and extend it into the future.

Example:

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X_train, y_train)
future = model.predict(X_future)

✅ Great for: Straight-line growth ⚠️ Not suitable for seasonality or patterns

🔄 4. ARIMA: AutoRegressive Integrated Moving Average

The powerhouse of time series forecasting.

ARIMA(p, d, q):

• p = autoregression (past values)
• d = differencing (to remove trends)
• q = moving average (past errors)

Python Example:

from statsmodels.tsa.arima.model import ARIMA

model = ARIMA(series, order=(2,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=10)

When to Use:

• Data with trends and no seasonality
• More accurate than simple averages

🗓 Choosing the Right Technique

🔍 Tools You Can Use

• 📊 Excel – Great for SMA/EMA
• 🐱 Python (pandas, statsmodels, scikit-learn) – Full flexibility
• 📉 Power BI – Built-in forecasting using line charts
• 🧠 Facebook Prophet – Seasonality-friendly alternative to ARIMA

🌟 Final Thoughts

Forecasting is a powerful skill—but it’s not about picking the fanciest model. It’s about understanding your data, testing approaches, and communicating insights clearly.

✅ What’s Next?

• 🔗 Try an ARIMA model in Python – GitHub Tutorial
• 📥 Subscribe to the “Analyze With Ankush” Newsletter
• 💬 Got questions? Comment below or connect with me on LinkedIn