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data Intermediate 21 lessons

Time Series Analysis

Predict future trends from historical data. Master ARIMA models, seasonality detection, and forecasting techniques using Python statsmodels.

Time is the most critical dimension in business data. This course teaches you to analyze data that changes over time to predict future outcomes. You will learn to decompose time series into trend, seasonality, and noise components. Master statistical models like ARIMA and SARIMA for rigorous forecasting, and explore modern libraries like Prophet. Whether predicting stock prices, website traffic, or retail sales, these skills enable you to answer the question 'what happens next?' with statistical confidence.

✅ 100% Free & Lifetime Access
⏱️ 5-Minute Lessons (Bite-sized learning)
🚀 21-Lesson Path (Independent modules)
📱 Mobile Friendly (Learn anywhere)
Quant Team
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Complete Course Syllabus

  • 1
    Time Series Basics
    Handling datetime indexes and resampling data frequencies.
  • 2
    Decomposition
    Separating data into trend, seasonal, and residual parts.
  • 3
    Stationarity
    Testing for constant mean and variance over time.
  • 4
    ARIMA Modeling
    Building AutoRegressive Integrated Moving Average models manually.
  • 5
    Forecasting Evaluation
    Backtesting models and measuring error rates effectively.

Estimated completion time: 21 lessons • Self-paced learning • Lifetime access

Career Outlook

Estimated Salary
$110k - $150k

Career Paths

Quantitative Analyst $120k-$160k
Demand Planner $90k-$130k
Data Scientist $110k-$150k

What You Will Learn

Decompose time series data into trend, seasonality, and residual components
Build and tune ARIMA models for accurate short-term forecasting
Detect stationarity using statistical tests like Augmented Dickey-Fuller
Evaluate forecast accuracy using metrics like MAE and RMSE
Visualize temporal data patterns to identify cycles and outliers

Skills You Will Gain

Forecasting ARIMA Modeling Seasonality Analysis Stationarity Python Statsmodels

Who Is This For

Financial Analysts
Demand Planners
Data Scientists

Prerequisites

Statistics Basics
Pandas

Time Series Analysis FAQs

Stock market?

Applicable concepts, but markets are highly unpredictable.

Math heavy?

Yes, requires understanding statistical distributions.

Prophet vs ARIMA?

We cover both: statistical rigor and ease-of-use.

Data requirements?

Needs consistent historical data without large gaps.

Need more info?
All Reork courses are 100% free, self-paced, and fully accessible on mobile devices. No prior experience is required. Read our full Platform FAQ.

Tech Stack & Tools

Python
Statsmodels
Pandas
Prophet
Matplotlib

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