Time Series Analysis Lab

Forecasting future values is a critical aspect of time series analysis, enabling businesses and researchers to make informed decisions based on historical data trends. In this lab, you will generate and evaluate time series forecasts, apply automated forecasting techniques, and refine models for improved accuracy.

Lab Overview

In this lab, you will:

• Explore time series data
• Clean and prepare the data for analysis
• Create forecasts using different models

Open the File

Open the file called workspace/tsanalysis.R. You will be working in this file throughout the lab.

Save Before Validation

Before validating a task, make sure to save the current document.

Dataset Information

The dataset for this lab is an Excel file called PersonalFinances.xlsx. This data has already been imported and is stored in a variable called finance_data.

🧹 Step 2: Handling Missing Values & Outliers

Missing values and outliers can significantly distort time series forecasts by introducing bias, inconsistency, or gaps in temporal structure.
Before building any models, it's critical to clean and validate your data to ensure it reflects the underlying patterns accurately.

This module focuses on data quality assurance as a foundation for reliable forecasting.

💡 Why It Matters

Many forecasting models — including ARIMA, ETS, and neural networks — assume that your time series is:

• Regularly spaced (no missing dates)
• Numerically complete (no NA values)
• Free from extreme anomalies (no dramatic outliers)

If you skip this step:

• Your models may fail to converge or throw errors.
• You may end up with misleading forecasts that reflect noise rather than true patterns.
• Seasonal or trend components may be misinterpreted due to data gaps or spikes.

By cleaning your data: ✅ You improve the quality and interpretability of your forecasts
✅ You reduce bias in model selection and evaluation
✅ You lay a reliable foundation for everything that follows in your time series workflow

Clean data isn't optional — it's a requirement for trustworthy time series forecasting. 🚀

🔄 Step 3: Apply Transformations for Stationarity

Many forecasting models, such as ARIMA, rely on a key assumption: stationarity.

A stationary time series has consistent statistical properties over time — its mean, variance, and autocorrelation do not change.
Real-world financial data often violates this assumption, especially when it contains trends, seasonal effects, or varying volatility.

This module focuses on applying transformations to make your time series stationary — a critical step before model training.

💡 Why It Matters

Time series models like ARIMA require your data to be stationary in order to make valid and reliable forecasts.
Non-stationary data can lead to:

• Misleading parameter estimates
• Poor out-of-sample forecast accuracy
• Spurious correlations and overfitting

By applying the correct transformations: ✅ You ensure your model assumptions are satisfied
✅ You improve your model’s ability to learn meaningful structure
✅ You reduce noise and increase interpretability

If your data isn't stationary, your model is likely guessing instead of forecasting. 🎯

🧠 Step 4: Build and Evaluate Time Series Models

Now that your data is clean and stationary, you're ready to apply forecasting models.

This module focuses on fitting, decomposing, and evaluating different time series models to capture trend, seasonality, and random noise in your data.
You'll use classic forecasting tools like ARIMA and ETS, explore seasonal decomposition, and apply diagnostics to compare model performance.

💡 Why It Matters

Forecasting is not just about choosing a model — it’s about choosing the right model for your data.

Each model type (ARIMA, ETS, STL) handles time series components differently.
By fitting and comparing multiple models, you can:

✅ Select the most accurate and interpretable forecasting approach
✅ Detect underfitting or overfitting through residual analysis
✅ Justify your model selection using statistical metrics

Choosing a model without evaluating it is like flying blind — diagnostics help you land forecasts with confidence. ✈️📉