Forecasting: Principles and Practice (2nd ed)
Rob J Hyndman and George Athanasopoulos
Monash University, Australia

Forecasting: Principles and Practice (3rd ed)
Rob J Hyndman and George Athanasopoulos
Monash University, Australia

Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on 20220208

This online version of the book was last updated on 4 February 2022.
The print version of the book (available from Amazon) was last updated on 31 May 2021.

読みながら、各章の参考文献を拝見。同じ文献を何度も出すのではなく、最初に紹介だけにしてもらえると嬉しいかも。中国語と韓国語が訳が出ているらしい。日本語、どうしよう。
2022年8月までは仕事がびっしり詰まっていて身動きができない。

どうしても訳したいという方がおみえなら、手伝いは可能。

最後のBibliographyが各章の加算かどうか未確認。
確認のためにこの記事にしたはず。

Rを使って確かめられるのは嬉しそう。

Forecasting: Principles and Practice 単語帳

Chapter 1 Getting started

Armstrong, J. S. (Ed.). (2001). Principles of forecasting: A handbook for researchers and practitioners. Kluwer Academic Publishers.

Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co.

Chapter 2 Time series graphics

Cleveland, W. S. (1993). Visualizing data. Hobart Press.

Unwin, A. (2015). Graphical data analysis with R. Chapman; Hall/CRC.

Chapter 3 Time series decomposition

Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. J. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–33. http://bit.ly/stl1990

Dagum, E. B., & Bianconcini, S. (2016). Seasonal adjustment methods and real time trend-cycle estimation. Springer.

Chapter 4 Time series features

Hyndman, R. J., Wang, E., & Laptev, N. (2015). Large-scale unusual time series detection. Proceedings of the IEEE International Conference on Data Mining, 1616–1619.

[DOI] https://ieeexplore.ieee.org/document/7395871

Kang, Y., Hyndman, R. J., & Smith-Miles, K. (2017). Visualising forecasting algorithm performance using time series instance spaces. International Journal of Forecasting, 33(2), 345–358.

[DOI]https://www.sciencedirect.com/science/article/abs/pii/S0169207016301030?via%3Dihub

Wang, X., Smith, K. A., & Hyndman, R. J. (2006). Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery, 13(3), 335–364.

[DOI] https://link.springer.com/article/10.1007/s10618-005-0039-x

Chapter 5 The forecaster’s toolbox

Gneiting, T., & Katzfuss, M. (2014). Probabilistic forecasting. Annual Review of Statistics and Its Application, 1(1), 125–151.
[DOI] https://www.annualreviews.org/doi/10.1146/annurev-statistics-062713-085831

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.
[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0169207006000239?via%3Dihub

Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co. 既出

Theodosiou, M. (2011). Forecasting monthly and quarterly time series using STL decomposition. International Journal of Forecasting, 27(4), 1178–1195. [DOI] https://www.sciencedirect.com/science/article/abs/pii/S0169207011000070?via%3Dihub

2nd.Chapter 3 The forecaster’s toolbox

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688.

[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0169207006000239?via%3Dihub

Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co. 既出

Chapter 6 Judgmental forecasts

Fildes, R., & Goodwin, P. (2007a). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces, 37(6), 570–576.
[DOI] https://pubsonline.informs.org/doi/abs/10.1287/inte.1070.0309

Fildes, R., & Goodwin, P. (2007b). Good and bad judgment in forecasting: Lessons from four companies. Foresight: The International Journal of Applied Forecasting, 8, 5–10.
https://www.researchgate.net/profile/Paul-Goodwin-4/publication/5055580_Good_and_Bad_Judgment_in_Forecasting_Lessons_from_Four_Companies/links/577285f608aeeec389540ba2/Good-and-Bad-Judgment-in-Forecasting-Lessons-from-Four-Companies?origin=publication_detail

Franses, P. H., & Legerstee, R. (2013). Do statistical forecasting models for SKU-level data benefit from including past expert knowledge? International Journal of Forecasting, 29(1), 80–87.
[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0169207012000842?via%3Dihub

Goodwin, P., & Wright, G. (2009). Decision analysis for management judgment (4th ed). John Wiley & Sons.

Green, K. C., & Armstrong, J. S. (2007). Structured analogies for forecasting. International Journal of Forecasting, 23(3), 365–376.

[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0169207007000696?via%3Dihub

Harvey, N. (2001). Improving judgment in forecasting. In J. S. Armstrong (Ed.), Principles of forecasting: A handbook for researchers and practitioners (pp. 59–80). Kluwer Academic Publishers.

[DOI] https://link.springer.com/chapter/10.1007/978-0-306-47630-3_4

Kahn, K. B. (2006). New product forecasting: An applied approach. M.E. Sharp.

Morwitz, V. G., Steckel, J. H., & Gupta, A. (2007). When do purchase intentions predict sales? International Journal of Forecasting, 23(3), 347–364.

[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0169207007000799?via%3Dihub

Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co. 既出

Önkal, D., Sayım, K. Z., & Gönül, M. S. (2013). Scenarios as channels of forecast advice. Technological Forecasting and Social Change, 80(4), 772–788.

[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0040162512002090?via%3Dihub

Rowe, G. (2007). A guide to Delphi. Foresight: The International Journal of Applied Forecasting, 8, 11–16.

Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15(4), 353–375.

[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0169207099000187?via%3Dihub

Sanders, N., Goodwin, P., Önkal, D., Gönül, M. S., Harvey, N., Lee, A., & Kjolso, L. (2005). When and how should statistical forecasts be judgmentally adjusted? Foresight: The International Journal of Applied Forecasting, 1(1), 5–23.

2nd, Chapter 4 Judgmental forecasts

Eroglu, C., & Croxton, K. L. (2010). Biases in judgmental adjustments of statistical forecasts: The role of individual differences. International Journal of Forecasting, 26(1), 116–133. [DOI]
Fildes, R., & Goodwin, P. (2007a). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces, 37(6), 570–576. [DOI]
Fildes, R., & Goodwin, P. (2007b). Good and bad judgment in forecasting: Lessons from four companies. Foresight: The International Journal of Applied Forecasting, (8), 5–10.
Franses, P. H., & Legerstee, R. (2013). Do statistical forecasting models for SKU-level data benefit from including past expert knowledge? International Journal of Forecasting, 29(1), 80–87. [DOI]
Goodwin, P., & Wright, G. (2009). Decision analysis for management judgment (4th ed). Chichester: John Wiley & Sons. [Amazon]
Green, K. C., & Armstrong, J. S. (2007). Structured analogies for forecasting. International Journal of Forecasting, 23(3), 365–376. [DOI]
Harvey, N. (2001). Improving judgment in forecasting. In J. S. Armstrong (Ed.), Principles of forecasting: A handbook for researchers and practitioners (pp. 59–80). Boston, MA: Kluwer Academic Publishers. [DOI]
Kahn, K. B. (2006). New product forecasting: An applied approach. M.E. Sharp. [Amazon]
Morwitz, V. G., Steckel, J. H., & Gupta, A. (2007). When do purchase intentions predict sales? International Journal of Forecasting, 23(3), 347–364. [DOI]
Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co.既出

Önkal, D., Sayım, K. Z., & Gönül, M. S. (2013). Scenarios as channels of forecast advice. Technological Forecasting and Social Change, 80(4), 772–788. [DOI]
Rowe, G. (2007). A guide to Delphi. Foresight: The International Journal of Applied Forecasting, (8), 11–16.
Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15(4), 353–375. [DOI]
Sanders, N., Goodwin, P., Önkal, D., Gönül, M. S., Harvey, N., Lee, A., & Kjolso, L. (2005). When and how should statistical forecasts be judgmentally adjusted? Foresight: The International Journal of Applied Forecasting, 1(1), 5–23. http://www.forecastpro.com/Trends/pdf/Nada%20Sanders%20Judgmental%20Adjustments%20to%20Statistical%20Forecasts%20July%202008.pdf

Chapter 7 Time series regression models

Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (2nd ed). Springer.

Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co.既出

Sheather, S. J. (2009). A modern approach to regression with R. Springer.

2nd, Chapter 5 Time series regression models

Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (2nd ed). New York, USA: Springer. [Amazon]

Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co. 既出

Sheather, S. J. (2009). A modern approach to regression with R. New York, USA: Springer. [Amazon]

2nd Chapter 6 Time series decomposition

Dagum, E. B., & Bianconcini, S. (2016). Seasonal adjustment methods and real time trend-cycle estimation. Springer. [Amazon]

Theodosiou, M. (2011). Forecasting monthly and quarterly time series using STL decomposition. International Journal of Forecasting, 27(4), 1178–1195. [DOI]

Chapter 8 Exponential smoothing

Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1–28.

[DOI] https://onlinelibrary.wiley.com/doi/10.1002/for.3980040103

Gardner, E. S. (2006). Exponential smoothing: The state of the art — Part II. International Journal of Forecasting, 22, 637–666.

[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0169207006000392?via%3Dihub

Hyndman, R. J., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Springer-Verlag. http://www.exponentialsmoothing.net

2nd Chapter 7 Exponential smoothing

Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1–28. [DOI]

Gardner, E. S. (2006). Exponential smoothing: The state of the art — Part II. International Journal of Forecasting, 22, 637–666. [DOI]

Hyndman, R. J., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Berlin: Springer-Verlag. http://www.exponentialsmoothing.net

Chapter 9 ARIMA models

Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden-Day.

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed). John Wiley & Sons.

Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting (3rd ed). Springer.

Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(1), 1–22.

[DOI] https://www.jstatsoft.org/article/view/v027i03

Peña, D., Tiao, G. C., & Tsay, R. S. (Eds.). (2001). A course in time series analysis. John Wiley & Sons.

Chapter 8 ARIMA models

Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. San Francisco: Holden-Day.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed). Hoboken, New Jersey: John Wiley & Sons. [Amazon]
Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting (3rd ed). New York, USA: Springer. [Amazon]
Peña, D., Tiao, G. C., & Tsay, R. S. (Eds.). (2001). A course in time series analysis. New York, USA: John Wiley & Sons. [Amazon]

Chapter 10 Dynamic regression models

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed). John Wiley & Sons. 既出

Pankratz, A. E. (1991). Forecasting with dynamic regression models. John Wiley & Sons.

2nd, Chapter 9 Dynamic regression models

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed). Hoboken, New Jersey: John Wiley & Sons. 既出
Pankratz, A. E. (1991). Forecasting with dynamic regression models. New York, USA: John Wiley & Sons. [Amazon]

Chapter 11 Forecasting hierarchical and grouped time series

Athanasopoulos, G., Ahmed, R. A., & Hyndman, R. J. (2009). Hierarchical forecasts for Australian domestic tourism. International Journal of Forecasting, 25, 146–166.

[DOI]https://www.sciencedirect.com/science/article/abs/pii/S0169207008000691?via%3Dihub

Athanasopoulos, George, Gamakumara, P., Panagiotelis, A., Hyndman, R. J., & Affan, M. (2020). Hierarchical forecasting. In P. Fuleky (Ed.), Macroeconomic forecasting in the era of big data (pp. 689–719). Springer.

[DOI] https://link.springer.com/chapter/10.1007/978-3-030-31150-6_21

Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Petropoulos, F. (2017). Forecasting with temporal hierarchies. European Journal of Operational Research, 262(1), 60–74.

[DOI]https://www.sciencedirect.com/science/article/abs/pii/S0377221717301911?via%3Dihub

Gross, C. W., & Sohl, J. E. (1990). Disaggregation methods to expedite product line forecasting. Journal of Forecasting, 9, 233–254.

[DOI] https://onlinelibrary.wiley.com/doi/10.1002/for.3980090304

Kourentzes, N., & Athanasopoulos, G. (2019). Cross-temporal coherent forecasts for Australian tourism. Annals of Tourism Research, 75, 393–409.

[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0160738319300167?via%3Dihub

Panagiotelis, A., Athanasopoulos, G., Gamakumara, P., & Hyndman, R. J. (2021). Forecast reconciliation: A geometric view with new insights on bias correction. International Journal of Forecasting, 37(1), 343–359.

[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0169207020300911?via%3Dihub

Panagiotelis, A., Gamakumara, P., Athanasopoulos, G., & Hyndman, R. J. (2020). Probabilistic forecast reconciliation: Properties, evaluation and score optimisation (Working Paper No. 26/20). Department of Econometrics & Business Statistics, Monash University. http://robjhyndman.com/publications/coherentprob/

Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019). Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization. Journal of the American Statistical Association, 114(526), 804–819.

[DOI] https://www.tandfonline.com/doi/full/10.1080/01621459.2018.1448825

Chapter 10 Forecasting hierarchical or grouped time series

Athanasopoulos, G., Ahmed, R. A., & Hyndman, R. J. (2009). Hierarchical forecasts for Australian domestic tourism. International Journal of Forecasting, 25, 146–166. [DOI]
Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Petropoulos, F. (2017). Forecasting with temporal hierarchies. European Journal of Operational Research, 262(1), 60–74. [DOI]
Gross, C. W., & Sohl, J. E. (1990). Disaggregation methods to expedite product line forecasting. Journal of Forecasting, 9, 233–254. [DOI]
Hyndman, R. J., Ahmed, R. A., Athanasopoulos, G., & Shang, H. L. (2011). Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579–2589. [DOI]
Hyndman, R. J., Lee, A., & Wang, E. (2016). Fast computation of reconciled forecasts for hierarchical and grouped time series. Computational Statistics and Data Analysis, 97, 16–32. [DOI]
Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019). Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization. Journal of the American Statistical Association, 114(526), 804–819. [DOI]

Chapter 12 Advanced forecasting methods

[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0169207015001120?via%3Dihub

Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388–427.

[DOI] https://www.sciencedirect.com/science/article/abs/pii/S0169207020300996?via%3Dihub

Lahiri, S. N. (2003). Resampling methods for dependent data. Springer Science & Business Media.

Pfaff, B. (2008). Analysis of integrated and cointegrated time series with R. Springer Science & Business Media.

Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45.

[DOI] https://www.tandfonline.com/doi/full/10.1080/00031305.2017.1380080

Chapter 11 Advanced forecasting methods

Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. International Journal of Forecasting, 32(2), 303–312. [DOI]
Crone, S. F., Hibon, M., & Nikolopoulos, K. (2011). Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction. International Journal of Forecasting, 27(3), 635–660. [DOI]
De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. J American Statistical Association, 106(496), 1513–1527. [DOI]
Lahiri, S. N. (2003). Resampling methods for dependent data. New York, USA: Springer Science & Business Media. [Amazon]
Pfaff, B. (2008). Analysis of integrated and cointegrated time series with R. New York, USA: Springer Science & Business Media. [Amazon]

Chapter 13 Some practical forecasting issues

Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co. 既出

Chapter 12 Some practical forecasting issues

Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co. 既出

Appendix: Using R

Appendix: For instructors

Appendix: Reviews

Translations

中国語と韓国語が訳が出ているらしい。

日本語、どうしよう。

About the authors

Buy a print or downloadable version

HELP

Bibliography

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Athanasopoulos, George, Gamakumara, P., Panagiotelis, A., Hyndman, R. J., & Affan, M. (2020). Hierarchical forecasting. In P. Fuleky (Ed.), Macroeconomic forecasting in the era of big data (pp. 689–719). Springer. [DOI]
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Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Petropoulos, F. (2017). Forecasting with temporal hierarchies. European Journal of Operational Research, 262(1), 60–74. [DOI]
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Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1–28. [DOI]
Gardner, E. S. (2006). Exponential smoothing: The state of the art — Part II. International Journal of Forecasting, 22, 637–666. [DOI]
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Goodwin, P., & Wright, G. (2009). Decision analysis for management judgment (4th ed). John Wiley & Sons. [Amazon]
Green, K. C., & Armstrong, J. S. (2007). Structured analogies for forecasting. International Journal of Forecasting, 23(3), 365–376. [DOI]
Gross, C. W., & Sohl, J. E. (1990). Disaggregation methods to expedite product line forecasting. Journal of Forecasting, 9, 233–254. [DOI]
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Harris, R., & Sollis, R. (2003). Applied time series modelling and forecasting. John Wiley & Sons. [Amazon]
Harvey, N. (2001). Improving judgment in forecasting. In J. S. Armstrong (Ed.), Principles of forecasting: A handbook for researchers and practitioners (pp. 59–80). Kluwer Academic Publishers. [DOI]
Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388–427. [DOI]
Holt, C. E. (1957). Forecasting seasonals and trends by exponentially weighted averages (O.N.R. Memorandum No. 52). Carnegie Institute of Technology, Pittsburgh USA. [DOI]
Hyndman, R. J., Ahmed, R. A., Athanasopoulos, G., & Shang, H. L. (2011). Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579–2589. [DOI]
Hyndman, R. J., & Fan, S. (2010). Density forecasting for long-term peak electricity demand. IEEE Transactions on Power Systems, 25(2), 1142–1153. [DOI]
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(1), 1–22. [DOI]
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. [DOI]
Hyndman, R. J., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Springer-Verlag. http://www.exponentialsmoothing.net
Hyndman, R. J., Wang, E., & Laptev, N. (2015). Large-scale unusual time series detection. Proceedings of the IEEE International Conference on Data Mining, 1616–1619. [DOI]
Izenman, A. J. (2008). Modern multivariate statistical techniques: Regression, classification and manifold learning. Springer. [Amazon]
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: With applications in R. Springer. [Amazon]
Kahn, K. B. (2006). New product forecasting: An applied approach. M.E. Sharp. [Amazon]
Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive perspective on risk taking. Management Science, 39(1), 17–31. [DOI]
Kang, Y., Hyndman, R. J., & Smith-Miles, K. (2017). Visualising forecasting algorithm performance using time series instance spaces. International Journal of Forecasting, 33(2), 345–358. [DOI]
Kourentzes, N., & Athanasopoulos, G. (2019). Cross-temporal coherent forecasts for Australian tourism. Annals of Tourism Research, 75, 393–409. [DOI]
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), 159–178. [DOI]
Lahiri, S. N. (2003). Resampling methods for dependent data. Springer Science & Business Media. [Amazon]
Lawrence, M., Goodwin, P., O’Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493–518. [DOI]
Lütkepohl, H. (2007). General-to-specific or specific-to-general modelling? An opinion on current econometric terminology. Journal of Econometrics, 136(1), 234–319. [DOI]
Morwitz, V. G., Steckel, J. H., & Gupta, A. (2007). When do purchase intentions predict sales? International Journal of Forecasting, 23(3), 347–364. [DOI]
Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co. [Amazon]
Önkal, D., Sayım, K. Z., & Gönül, M. S. (2013). Scenarios as channels of forecast advice. Technological Forecasting and Social Change, 80(4), 772–788. [DOI]
Panagiotelis, A., Athanasopoulos, G., Gamakumara, P., & Hyndman, R. J. (2021). Forecast reconciliation: A geometric view with new insights on bias correction. International Journal of Forecasting, 37(1), 343–359. [DOI]
Panagiotelis, A., Gamakumara, P., Athanasopoulos, G., & Hyndman, R. J. (2020). Probabilistic forecast reconciliation: Properties, evaluation and score optimisation (Working Paper No. 26/20). Department of Econometrics & Business Statistics, Monash University. http://robjhyndman.com/publications/coherentprob/
Pankratz, A. E. (1991). Forecasting with dynamic regression models. John Wiley & Sons. [Amazon]
Pegels, C. C. (1969). Exponential forecasting: Some new variations. Management Science, 15(5), 311–315. [DOI]
Peña, D., Tiao, G. C., & Tsay, R. S. (Eds.). (2001). A course in time series analysis. John Wiley & Sons. [Amazon]
Pfaff, B. (2008). Analysis of integrated and cointegrated time series with R. Springer Science & Business Media. [Amazon]
Randall, D. M., & Wolff, J. A. (1994). The time interval in the intention-behaviour relationship: Meta-analysis. British Journal of Social Psychology, 33(4), 405–418. [DOI]
Rowe, G. (2007). A guide to Delphi. Foresight: The International Journal of Applied Forecasting, 8, 11–16.
Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15(4), 353–375. [DOI]
Sanders, N., Goodwin, P., Önkal, D., Gönül, M. S., Harvey, N., Lee, A., & Kjolso, L. (2005). When and how should statistical forecasts be judgmentally adjusted? Foresight: The International Journal of Applied Forecasting, 1(1), 5–23.
Sheather, S. J. (2009). A modern approach to regression with R. Springer. [Amazon]
Shenstone, L., & Hyndman, R. J. (2005). Stochastic models underlying Croston’s method for intermittent demand forecasting. Journal of Forecasting, 24(6), 389–402. [DOI]
Taylor, J. W. (2003). Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting, 19(4), 715–725. [DOI]
Taylor, S. J., & Letham, B. (2018). Forecasting at scale. The American Statistician, 72(1), 37–45. [DOI]
Theodosiou, M. (2011). Forecasting monthly and quarterly time series using STL decomposition. International Journal of Forecasting, 27(4), 1178–1195. [DOI]
Unwin, A. (2015). Graphical data analysis with R. Chapman; Hall/CRC. [Amazon]
Wang, X., Smith, K. A., & Hyndman, R. J. (2006). Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery, 13(3), 335–364. [DOI]
Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019). Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization. Journal of the American Statistical Association, 114(526), 804–819. [DOI]
Winkler, R. L. (1972). A decision-theoretic approach to interval estimation. Journal of the American Statistical Association, 67(337), 187–191. [DOI]
Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324–342. [DOI]
Young, P. C., Pedregal, D. J., & Tych, W. (1999). Dynamic harmonic regression. Journal of Forecasting, 18, 369–394. [DOI]

2nd Bibliography

Armstrong, J. S. (1978). Long-range forecasting: From crystal ball to computer. John Wiley & Sons. [Amazon]
Armstrong, J. S. (Ed.). (2001). Principles of forecasting: A handbook for researchers and practitioners. Kluwer Academic Publishers. [Amazon]
Athanasopoulos, G., Ahmed, R. A., & Hyndman, R. J. (2009). Hierarchical forecasts for Australian domestic tourism. International Journal of Forecasting, 25, 146–166. [DOI]
Athanasopoulos, G., & Hyndman, R. J. (2008). Modelling and forecasting Australian domestic tourism. Tourism Management, 29(1), 19–31. [DOI]
Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Petropoulos, F. (2017). Forecasting with temporal hierarchies. European Journal of Operational Research, 262(1), 60–74. [DOI]
Athanasopoulos, G., Poskitt, D. S., & Vahid, F. (2012). Two canonical VARMA forms: Scalar component models vis-à-vis the echelon form. Econometric Reviews, 31(1), 60–83. [DOI]
Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. Operational Research Quarterly, 20(4), 451–468. [DOI]
Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. International Journal of Forecasting, 32(2), 303–312. [DOI]
Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics and Data Analysis, 120, 70–83. [DOI]
Bickel, P. J., & Doksum, K. A. (1981). An analysis of transformations revisited. Journal of the American Statistical Association, 76(374), 296–311.
Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 26(2), 211–252. [DOI]
Box, G. E. P., & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. San Francisco: Holden-Day.
Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control (5th ed). Hoboken, New Jersey: John Wiley & Sons. [Amazon]
Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting (3rd ed). New York, USA: Springer. [Amazon]
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Buehler, R., Messervey, D., & Griffin, D. (2005). Collaborative planning and prediction: Does group discussion affect optimistic biases in time estimation? Organizational Behavior and Human Decision Processes, 97(1), 47–63. [DOI]
Christou, V., & Fokianos, K. (2015). On count time series prediction. Journal of Statistical Computation and Simulation, 85(2), 357–373. [DOI]
Clemen, R. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559–583. [DOI]
Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. J. (1990). STL: A seasonal-trend decomposition procedure based on loess. Journal of Official Statistics, 6(1), 3–33. http://bit.ly/stl1990
Cleveland, W. S. (1993). Visualizing data. Hobart Press. [Amazon]
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Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23(3), 289–303. [DOI]
Dagum, E. B., & Bianconcini, S. (2016). Seasonal adjustment methods and real time trend-cycle estimation. Springer. [Amazon]
De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. J American Statistical Association, 106(496), 1513–1527. [DOI]
Eroglu, C., & Croxton, K. L. (2010). Biases in judgmental adjustments of statistical forecasts: The role of individual differences. International Journal of Forecasting, 26(1), 116–133. [DOI]
Fan, S., & Hyndman, R. J. (2012). Short-term load forecasting based on a semi-parametric additive model. IEEE Transactions on Power Systems, 27(1), 134–141. [DOI]
Fildes, R., & Goodwin, P. (2007a). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces, 37(6), 570–576. [DOI]
Fildes, R., & Goodwin, P. (2007b). Good and bad judgment in forecasting: Lessons from four companies. Foresight: The International Journal of Applied Forecasting, (8), 5–10.
Franses, P. H., & Legerstee, R. (2013). Do statistical forecasting models for SKU-level data benefit from including past expert knowledge? International Journal of Forecasting, 29(1), 80–87. [DOI]
Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1–28. [DOI]
Gardner, E. S. (2006). Exponential smoothing: The state of the art — Part II. International Journal of Forecasting, 22, 637–666. [DOI]
Gardner, E. S., & McKenzie, E. (1985). Forecasting trends in time series. Management Science, 31(10), 1237–1246. [DOI]
Goodwin, P., & Wright, G. (2009). Decision analysis for management judgment (4th ed). Chichester: John Wiley & Sons. [Amazon]
Green, K. C., & Armstrong, J. S. (2007). Structured analogies for forecasting. International Journal of Forecasting, 23(3), 365–376. [DOI]
Gross, C. W., & Sohl, J. E. (1990). Disaggregation methods to expedite product line forecasting. Journal of Forecasting, 9, 233–254. [DOI]
Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey methodology (2nd ed). John Wiley & Sons. [Amazon]
Hamilton, J. D. (1994). Time series analysis. Princeton University Press, Princeton. [Amazon]
Harrell, F. E. (2015). Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis (2nd ed). New York, USA: Springer. [Amazon]
Harris, R., & Sollis, R. (2003). Applied time series modelling and forecasting. Chichester, UK: John Wiley & Sons. [Amazon]
Harvey, N. (2001). Improving judgment in forecasting. In J. S. Armstrong (Ed.), Principles of forecasting: A handbook for researchers and practitioners (pp. 59–80). Boston, MA: Kluwer Academic Publishers. [DOI]
Holt, C. E. (1957). Forecasting seasonals and trends by exponentially weighted averages (O.N.R. Memorandum No. 52). Carnegie Institute of Technology, Pittsburgh USA. [DOI]
Hyndman, R. J., Ahmed, R. A., Athanasopoulos, G., & Shang, H. L. (2011). Optimal combination forecasts for hierarchical time series. Computational Statistics and Data Analysis, 55(9), 2579–2589. [DOI]
Hyndman, R. J., & Fan, S. (2010). Density forecasting for long-term peak electricity demand. IEEE Transactions on Power Systems, 25(2), 1142–1153. [DOI]
Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(1), 1–22. [DOI]
Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. [DOI]
Hyndman, R. J., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Berlin: Springer-Verlag. http://www.exponentialsmoothing.net
Hyndman, R. J., Koehler, A. B., Snyder, R. D., & Grose, S. (2002). A state space framework for automatic forecasting using exponential smoothing methods. International Journal of Forecasting, 18(3), 439–454. [DOI]
Hyndman, R. J., Lee, A., & Wang, E. (2016). Fast computation of reconciled forecasts for hierarchical and grouped time series. Computational Statistics and Data Analysis, 97, 16–32. [DOI]
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2014). An introduction to statistical learning: With applications in R. New York: Springer. [Amazon]
Kahn, K. B. (2006). New product forecasting: An applied approach. M.E. Sharp. [Amazon]
Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive perspective on risk taking. Management Science, 39(1), 17–31. [DOI]
Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), 159–178. [DOI]
Lahiri, S. N. (2003). Resampling methods for dependent data. New York, USA: Springer Science & Business Media. [Amazon]
Lawrence, M., Goodwin, P., O’Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493–518. [DOI]
Lütkepohl, H. (2005). New introduction to multiple time series analysis. Berlin: Springer-Verlag. [Amazon]
Lütkepohl, H. (2007). General-to-specific or specific-to-general modelling? An opinion on current econometric terminology. Journal of Econometrics, 136(1), 234–319. [DOI]
Morwitz, V. G., Steckel, J. H., & Gupta, A. (2007). When do purchase intentions predict sales? International Journal of Forecasting, 23(3), 347–364. [DOI]
Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co. [Amazon]
Önkal, D., Sayım, K. Z., & Gönül, M. S. (2013). Scenarios as channels of forecast advice. Technological Forecasting and Social Change, 80(4), 772–788. [DOI]
Pankratz, A. E. (1991). Forecasting with dynamic regression models. New York, USA: John Wiley & Sons. [Amazon]
Pegels, C. C. (1969). Exponential forecasting: Some new variations. Management Science, 15(5), 311–315. [DOI]
Peña, D., Tiao, G. C., & Tsay, R. S. (Eds.). (2001). A course in time series analysis. New York, USA: John Wiley & Sons. [Amazon]
Pfaff, B. (2008). Analysis of integrated and cointegrated time series with R. New York, USA: Springer Science & Business Media. [Amazon]
Randall, D. M., & Wolff, J. A. (1994). The time interval in the intention-behaviour relationship: Meta-analysis. British Journal of Social Psychology, 33(4), 405–418. [DOI]
Rowe, G. (2007). A guide to Delphi. Foresight: The International Journal of Applied Forecasting, (8), 11–16.
Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15(4), 353–375. [DOI]
Sanders, N., Goodwin, P., Önkal, D., Gönül, M. S., Harvey, N., Lee, A., & Kjolso, L. (2005). When and how should statistical forecasts be judgmentally adjusted? Foresight: The International Journal of Applied Forecasting, 1(1), 5–23. http://www.forecastpro.com/Trends/pdf/Nada%20Sanders%20Judgmental%20Adjustments%20to%20Statistical%20Forecasts%20July%202008.pdf
Sheather, S. J. (2009). A modern approach to regression with R. New York, USA: Springer. [Amazon]
Shenstone, L., & Hyndman, R. J. (2005). Stochastic models underlying Croston’s method for intermittent demand forecasting. Journal of Forecasting, 24(6), 389–402. [DOI]
Taylor, J. W. (2003). Exponential smoothing with a damped multiplicative trend. International Journal of Forecasting, 19(4), 715–725. [DOI]
Theodosiou, M. (2011). Forecasting monthly and quarterly time series using STL decomposition. International Journal of Forecasting, 27(4), 1178–1195. [DOI]
Unwin, A. (2015). Graphical data analysis with R. Chapman; Hall/CRC. [Amazon]
Wang, X., Smith, K. A., & Hyndman, R. J. (2006). Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery, 13(3), 335–364. [DOI]
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis (2nd ed). Springer. [Amazon]
Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019). Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization. Journal of the American Statistical Association, 114(526), 804–819. [DOI]
Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management Science, 6(3), 324–342. [DOI]
Young, P. C., Pedregal, D. J., & Tych, W. (1999). Dynamic harmonic regression. Journal of Forecasting, 18, 369–394. [DOI]

作業

一覧確認

参考資料

私のAdvent Calendar 2022 ーーはじめたきっかけ、1月のふりかえり、今後の展望

自己参照

今、AdventCalendar2021を書いている理由

Forecasting: Principles and Practice, Rob J Hyndman and George Athanasopoulos

Chapter 1 Getting started

Chapter 2 Time series graphics

Chapter 3 Time series decomposition

Chapter 4 Time series features

Chapter 5 The forecaster’s toolbox

2nd.Chapter 3 The forecaster’s toolbox

Chapter 6 Judgmental forecasts

2nd, Chapter 4 Judgmental forecasts

Chapter 7 Time series regression models

2nd, Chapter 5 Time series regression models

2nd Chapter 6 Time series decomposition

Chapter 8 Exponential smoothing

2nd Chapter 7 Exponential smoothing

Chapter 9 ARIMA models

Chapter 8 ARIMA models

Chapter 10 Dynamic regression models

2nd, Chapter 9 Dynamic regression models

Chapter 11 Forecasting hierarchical and grouped time series

Chapter 10 Forecasting hierarchical or grouped time series

Chapter 12 Advanced forecasting methods

Chapter 11 Advanced forecasting methods

Chapter 13 Some practical forecasting issues

Chapter 12 Some practical forecasting issues

Appendix: Using R

Appendix: For instructors

Appendix: Reviews

Translations

About the authors

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Bibliography

2nd Bibliography

作業

参考資料

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