Bibliography & Suggested Readings

B.1 Foundational Time Series and Forecasting

These works establish the core statistical and methodological foundations of forecasting.

• Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015).
  Time Series Analysis: Forecasting and Control (5th ed.). Wiley.
• Hyndman, R. J., & Athanasopoulos, G. (2021).
  Forecasting: Principles and Practice (3rd ed.). OTexts.
• Chatfield, C. (2000).
  Time-Series Forecasting. Chapman & Hall/CRC.
• Hamilton, J. D. (1994).
  Time Series Analysis. Princeton University Press.
• Shumway, R. H., & Stoffer, D. S. (2017).
  Time Series Analysis and Its Applications (4th ed.). Springer.

B.2 Smoothing, Decomposition, and Exponential Models

Key references for signal extraction and interpretable forecasting structures.

• Holt, C. C. (1957).
  Forecasting seasonals and trends by exponentially weighted moving averages.
• Winters, P. R. (1960).
  Forecasting sales by exponentially weighted moving averages.
• Gardner, E. S. (1985).
  Exponential smoothing: The state of the art. Journal of Forecasting.
• Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990).
  STL: A seasonal-trend decomposition procedure. Journal of Official Statistics.

B.3 Regression and Time Series Linear Models (TSLM)

Connections between forecasting and explanatory modeling.

• Draper, N. R., & Smith, H. (1998).
  Applied Regression Analysis (3rd ed.). Wiley.
• Kutner, M. H., Nachtsheim, C. J., & Neter, J. (2004).
  Applied Linear Regression Models. McGraw-Hill.
• Wooldridge, J. M. (2016).
  Introductory Econometrics: A Modern Approach (6th ed.). Cengage.

B.4 Statistical Learning and Machine Learning

Foundations for feature-based forecasting and predictive modeling.

• Hastie, T., Tibshirani, R., & Friedman, J. (2009).
  The Elements of Statistical Learning (2nd ed.). Springer.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021).
  An Introduction to Statistical Learning (2nd ed.). Springer.
• Breiman, L. (2001).
  Random forests. Machine Learning, 45(1), 5–32.
• Friedman, J. H. (2001).
  Greedy function approximation: A gradient boosting machine. Annals of Statistics.

B.5 Deep Learning and Sequential Models

Modern approaches for large-scale and complex temporal patterns.

• Goodfellow, I., Bengio, Y., & Courville, A. (2016).
  Deep Learning. MIT Press.
• Hochreiter, S., & Schmidhuber, J. (1997).
  Long short-term memory. Neural Computation.
• Vaswani, A., et al. (2017).
  Attention is all you need. NeurIPS.
• Lim, B., & Zohren, S. (2021).
  Time-series forecasting with deep learning: A survey. Philosophical Transactions A.

B.6 Forecast Evaluation, Validation, and Trust

Critical works on assessing forecasting systems and building reliability.

• Hyndman, R. J., & Koehler, A. B. (2006).
  Another look at measures of forecast accuracy. International Journal of Forecasting.
• Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018).
  The M4 competition. International Journal of Forecasting.
• Diebold, F. X. (2015).
  Comparing Predictive Accuracy. Princeton University Press.

B.7 Forecasting in Business and Decision Contexts

Bridging forecasting methods with managerial decision-making.

• Armstrong, J. S. (Ed.). (2001).
  Principles of Forecasting. Springer.
• Silver, N. (2012).
  The Signal and the Noise. Penguin.
• Tetlock, P. E., & Gardner, D. (2015).
  Superforecasting. Crown.
• Davenport, T. H., & Harris, J. G. (2017).
  Competing on Analytics. Harvard Business Review Press.

B.8 AI, Data Science, and Organizational Decision Systems

Understanding forecasting within broader analytics and AI systems.

• Provost, F., & Fawcett, T. (2013).
  Data Science for Business. O’Reilly.
• Russell, S., & Norvig, P. (2021).
  Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
• Molnar, C. (2022).
  Interpretable Machine Learning.
• Amershi, S., et al. (2019).
  Software engineering for machine learning. ICSE.

B.9 Suggested Readings by Learning Path

To support different types of learners, the following guided reading paths are recommended.

For Conceptual Understanding (Beginner-Friendly)

• Hyndman & Athanasopoulos — Forecasting: Principles and Practice
• Silver — The Signal and the Noise

Focus: intuition, interpretation, and practical understanding

For Technical Depth (Intermediate–Advanced)

• Box et al. — Time Series Analysis
• Hastie et al. — Elements of Statistical Learning

Focus: models, assumptions, and analytical rigor

For Machine Learning and AI Integration

• James et al. — Introduction to Statistical Learning
• Goodfellow et al. — Deep Learning

Focus: modern predictive methods and scalability

For Decision and Business Context

• Davenport & Harris — Competing on Analytics
• Tetlock & Gardner — Superforecasting

Focus: how forecasting supports real decisions