Forecasting: Principles and Practice (2nd ed)
Rob J Hyndman and George Athanasopoulos
Monash University, Australia
https://otexts.com/fpp2/

Forecasting: Principles and Practice (3rd ed)
Rob J Hyndman and George Athanasopoulos
Monash University, Australia
https://otexts.com/fpp3/
https://www.youtube.com/watch?v=7TglVxNgbKQ

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
https://qiita.com/kaizen_nagoya/items/810d1065c7ec321604d5

<この項は書きかけです。順次追記します。>
This article is not completed. I will add some words in order.

Chapter 1 Getting started

Armstrong, J. S. (Ed.). (2001). Principles of forecasting: A handbook for researchers and practitioners. Kluwer Academic Publishers.
https://www.amazon.co.jp/dp/0792379306?tag=otexts-22&geniuslink=true
https://www.gwern.net/docs/prediction/2001-armstrong-principlesforecasting.pdf

Ord, J. K., Fildes, R., & Kourentzes, N. (2017). Principles of business forecasting (2nd ed.). Wessex Press Publishing Co.
https://www.amazon.co.jp/dp/0999064916?tag=otexts-22&geniuslink=true

Chapter 2 Time series graphics

Cleveland, W. S. (1993). Visualizing data. Hobart Press.
https://www.amazon.co.jp/dp/0963488406?tag=otexts-22&geniuslink=true

Unwin, A. (2015). Graphical data analysis with R. Chapman; Hall/CRC.
https://www.amazon.co.jp/dp/1498715230?tag=otexts-22&geniuslink=true

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.
https://www.amazon.co.jp/dp/3319318209?tag=otexts-22&geniuslink=true

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

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] https://www.sciencedirect.com/science/article/abs/pii/S0169207009000442?via%3Dihub

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.
https://www.amazon.co.jp/dp/0470714395?tag=otexts-22&geniuslink=true

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.
https://www.amazon.co.jp/dp/0765616092?tag=otexts-22&geniuslink=true

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.
https://www.amazon.co.jp/dp/3319194240?tag=otexts-22&geniuslink=true

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.
https://www.amazon.co.jp/dp/0387096078?tag=otexts-22&geniuslink=true

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

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. [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.

https://www.wiley.com/en-au/Time+Series+Analysis:+Forecasting+and+Control,+5th+Edition-p-9781118675021
https://www.amazon.co.uk/Time-analysis-Forecasting-control-Holden-Day/dp/0816211043

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.
https://www.amazon.co.jp/dp/3319298526?tag=otexts-22&geniuslink=true

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.
https://www.amazon.co.jp/dp/047136164X?tag=otexts-22&geniuslink=true

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.
https://www.amazon.co.jp/dp/0471615285?tag=otexts-22&geniuslink=true

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

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] 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.
https://www.amazon.co.jp/dp/0387009280?tag=otexts-22&geniuslink=true

Pfaff, B. (2008). Analysis of integrated and cointegrated time series with R. Springer Science & Business Media.
https://www.amazon.co.jp/dp/0387759662?tag=otexts-22&geniuslink=true

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
https://otexts.com/fpp2/extrafiles/ChulwalarCase.zip

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

Anscombe, F. J. (1973). Graphs in statistical analysis. The American Statistician, 27(1), 17–21. [DOI]
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, 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]
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. [DOI]
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. 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. [Amazon]
Brockwell, P. J., & Davis, R. A. (2016). Introduction to time series and forecasting (3rd ed). Springer. [Amazon]
Brown, R. G. (1959). Statistical forecasting for inventory control. McGraw/Hill.
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]
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]
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]
Gneiting, T., & Katzfuss, M. (2014). Probabilistic forecasting. Annual Review of Statistics and Its Application, 1(1), 125–151. [DOI]
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]
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]
Guerrero, V. M. (1993). Time-series analysis supported by power transformations. Journal of Forecasting, 12(1), 37–48. [DOI]
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). Springer. [Amazon]
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]
Brown, R. G. (1959). Statistical forecasting for inventory control. McGraw/Hill.
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]
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]
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
https://qiita.com/torifukukaiou/items/891db4e40a7f6194af56

自己参照

今、Calendar2021を書いている理由
https://qiita.com/kaizen_nagoya/items/dca73e903785bea2a106

関連資料

' @kazuo_reve 私が効果を確認した「小川メソッド」
https://qiita.com/kazuo_reve/items/a3ea1d9171deeccc04da

' @kazuo_reve 新人の方によく展開している有益な情報
https://qiita.com/kazuo_reve/items/d1a3f0ee48e24bba38f1

' @kazuo_reve Vモデルについて勘違いしていたと思ったこと
https://qiita.com/kazuo_reve/items/46fddb094563bd9b2e1e

Engineering Festa 2024前に必読記事一覧

登壇直後版色使い(JIS安全色) Qiita Engineer Festa 2023〜私しか得しないニッチな技術でLT〜スライド編 0.15
https://qiita.com/kaizen_nagoya/items/f0d3070d839f4f735b2b

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

逆も真：社会人が最初に確かめるとよいこと。OSEK(69)、Ethernet(59)
https://qiita.com/kaizen_nagoya/items/39afe4a728a31b903ddc

統計の嘘。仮説（127）
https://qiita.com/kaizen_nagoya/items/63b48ecf258a3471c51b

自分の言葉だけで論理展開できるのが天才なら、文章の引用だけで論理展開できるのが秀才だ。仮説（136）
https://qiita.com/kaizen_nagoya/items/97cf07b9e24f860624dd

参考文献駆動執筆(references driven writing)・デンソークリエイト編
https://qiita.com/kaizen_nagoya/items/b27b3f58b8bf265a5cd1

「何を」よりも「誰を」。１０年後のために今見習いたい人たち
https://qiita.com/kaizen_nagoya/items/8045978b16eb49d572b2

Qiitaの記事に３段階または５段階で到達するための方法
https://qiita.com/kaizen_nagoya/items/6e9298296852325adc5e

出力(output)と呼ばないで。これは状態(state)です。
https://qiita.com/kaizen_nagoya/items/80b8b5913b2748867840

coding (101) 一覧を作成し始めた。omake:最近のQiitaで表示しない5つの事象
https://qiita.com/kaizen_nagoya/items/20667f09f19598aedb68

あなたは「勘違いまとめ」から、勘違いだと言っていることが勘違いだといくつ見つけられますか。人間の間違い(human error(125))の種類と対策
https://qiita.com/kaizen_nagoya/items/ae391b77fffb098b8fb4

プログラマの「プログラムが書ける」思い込みは強みだ。３つの理由。仮説（168）統計と確率(17) , OSEK(79)
https://qiita.com/kaizen_nagoya/items/bc5dd86e414de402ec29

出力(output)と呼ばないで。これは状態(state)です。
https://qiita.com/kaizen_nagoya/items/80b8b5913b2748867840

これからの情報伝達手段の在り方について考えてみよう。炎上と便乗。
https://qiita.com/kaizen_nagoya/items/71a09077ac195214f0db

ISO/IEC JTC1 SC7 Software and System Engineering
https://qiita.com/kaizen_nagoya/items/48b43f0f6976a078d907

アクセシビリティの知見を発信しよう！（再び）
https://qiita.com/kaizen_nagoya/items/03457eb9ee74105ee618

統計論及確率論輪講（再び）
https://qiita.com/kaizen_nagoya/items/590874ccfca988e85ea3

読者の心をグッと惹き寄せる7つの魔法
https://qiita.com/kaizen_nagoya/items/b1b5e89bd5c0a211d862

「@kazuo_reve 新人の方によく展開している有益な情報」確認一覧
https://qiita.com/kaizen_nagoya/items/b9380888d1e5a042646b

ソースコードで議論しよう。日本語で議論するの止めましょう（あるプログラミング技術の議論報告）
https://qiita.com/kaizen_nagoya/items/8b9811c80f3338c6c0b0

脳内コンパイラの3つの危険
https://qiita.com/kaizen_nagoya/items/7025cf2d7bd9f276e382

心理学の本を読むよりはコンパイラ書いた方がよくね。仮説（34）
https://qiita.com/kaizen_nagoya/items/fa715732cc148e48880e

NASAを超えるつもりがあれば読んでください。
https://qiita.com/kaizen_nagoya/items/e81669f9cb53109157f6

データサイエンティストの気づき!「勉強して仕事に役立てない人。大嫌い!!」『それ自分かも?』ってなった!!!
https://qiita.com/kaizen_nagoya/items/d85830d58d8dd7f71d07

「ぼくの好きな先生」「人がやらないことをやれ」プログラマになるまで。仮説（37）　
https://qiita.com/kaizen_nagoya/items/53e4bded9fe5f724b3c4

なぜ経済学徒を辞め、計算機屋になったか（経済学部入学前・入学後・卒業後対応）転職(1)
https://qiita.com/kaizen_nagoya/items/06335a1d24c099733f64

プログラミング言語教育のXYZ。仮説（52）
https://qiita.com/kaizen_nagoya/items/1950c5810fb5c0b07be4

【24卒向け】9ヶ月後に年収１０００万円を目指す。二つの関門と三つの道。
https://qiita.com/kaizen_nagoya/items/fb5bff147193f726ad25

「【25卒向け】Qiita Career Meetup for STUDENT」予習の勧め
https://qiita.com/kaizen_nagoya/items/00eadb8a6e738cb6336f

大学入試不合格でも筆記試験のない大学に入って卒業できる。卒業しなくても博士になれる。
https://qiita.com/kaizen_nagoya/items/74adec99f396d64b5fd5

全世界の不登校の子供たち「博士論文」を書こう。世界子供博士論文遠隔実践中心安全(99)
https://qiita.com/kaizen_nagoya/items/912d69032c012bcc84f2

小川メソッド　覚え（書きかけ）
https://qiita.com/kaizen_nagoya/items/3593d72eca551742df68

DoCAP（ドゥーキャップ）って何ですか？
https://qiita.com/kaizen_nagoya/items/47e0e6509ab792c43327

views 20,000越え自己記事一覧
https://qiita.com/kaizen_nagoya/items/58e8bd6450957cdecd81

Views１万越え、もうすぐ１万記事一覧最近いいねをいただいた213記事
https://qiita.com/kaizen_nagoya/items/d2b805717a92459ce853

自己記事一覧

Qiitaで逆リンクを表示しなくなったような気がする。時々、スマフォで表示するとあらわっることがあり、完全に削除したのではなさそう。

４月以降、せっせとリンクリストを作り、統計を取って確率を説明しようとしている。
2025年２月末を目標にしている。

物理記事　上位100
https://qiita.com/kaizen_nagoya/items/66e90fe31fbe3facc6ff

量子(0) 計算機, 量子力学
https://qiita.com/kaizen_nagoya/items/1cd954cb0eed92879fd4

数学関連記事１００
https://qiita.com/kaizen_nagoya/items/d8dadb49a6397e854c6d

統計(0)一覧
https://qiita.com/kaizen_nagoya/items/80d3b221807e53e88aba

図(0) state, sequence and timing. UML and お絵描き
https://qiita.com/kaizen_nagoya/items/60440a882146aeee9e8f

品質一覧
https://qiita.com/kaizen_nagoya/items/2b99b8e9db6d94b2e971

言語・文学記事　１００
https://qiita.com/kaizen_nagoya/items/42d58d5ef7fb53c407d6

医工連携関連記事一覧
https://qiita.com/kaizen_nagoya/items/6ab51c12ba51bc260a82

自動車　記事　１００
https://qiita.com/kaizen_nagoya/items/f7f0b9ab36569ad409c5

通信記事１００
https://qiita.com/kaizen_nagoya/items/1d67de5e1cd207b05ef7

日本語（０）一欄
https://qiita.com/kaizen_nagoya/items/7498dcfa3a9ba7fd1e68

英語(0) 一覧
https://qiita.com/kaizen_nagoya/items/680e3f5cbf9430486c7d

転職(0)一覧
https://qiita.com/kaizen_nagoya/items/f77520d378d33451d6fe

仮説（0）一覧（目標100現在40）
https://qiita.com/kaizen_nagoya/items/f000506fe1837b3590df

音楽　一覧(0)
https://qiita.com/kaizen_nagoya/items/b6e5f42bbfe3bbe40f5d

「@kazuo_reve 新人の方によく展開している有益な情報」確認一覧
https://qiita.com/kaizen_nagoya/items/b9380888d1e5a042646b

Qiita(0)Qiita関連記事一覧（自分）
https://qiita.com/kaizen_nagoya/items/58db5fbf036b28e9dfa6

鉄道（０）鉄道のシステム考察はてっちゃんがてつだってくれる
https://qiita.com/kaizen_nagoya/items/26bda595f341a27901a0

安全（0）安全工学シンポジウムに向けて: 21
https://qiita.com/kaizen_nagoya/items/c5d78f3def8195cb2409

一覧の一覧( The directory of directories of mine.) Qiita(100)
https://qiita.com/kaizen_nagoya/items/7eb0e006543886138f39

Ethernet 記事一覧　Ethernet(0)
https://qiita.com/kaizen_nagoya/items/88d35e99f74aefc98794

Wireshark 一覧 wireshark(0)、Ethernet(48)
https://qiita.com/kaizen_nagoya/items/fbed841f61875c4731d0

線網（Wi-Fi）空中線(antenna)(0) 記事一覧(118/300目標)
https://qiita.com/kaizen_nagoya/items/5e5464ac2b24bd4cd001

OSEK OS設計の基礎　OSEK(100)
https://qiita.com/kaizen_nagoya/items/7528a22a14242d2d58a3

Error一覧 error(0)
https://qiita.com/kaizen_nagoya/items/48b6cbc8d68eae2c42b8

++ Support(0)　
https://qiita.com/kaizen_nagoya/items/8720d26f762369a80514

Coding(0) Rules, C, Secure, MISRA and so on
https://qiita.com/kaizen_nagoya/items/400725644a8a0e90fbb0

coding (101) 一覧を作成し始めた。omake:最近のQiitaで表示しない5つの事象
https://qiita.com/kaizen_nagoya/items/20667f09f19598aedb68

プログラマによる、プログラマのための、統計(0)と確率のプログラミングとその後
https://qiita.com/kaizen_nagoya/items/6e9897eb641268766909

なぜdockerで機械学習するか書籍・ソース一覧作成中 (目標100)
https://qiita.com/kaizen_nagoya/items/ddd12477544bf5ba85e2

言語処理100本ノックをdockerで。python覚えるのに最適。:10+12
https://qiita.com/kaizen_nagoya/items/7e7eb7c543e0c18438c4

プログラムちょい替え（0）一覧:4件
https://qiita.com/kaizen_nagoya/items/296d87ef4bfd516bc394

Python(0)記事をまとめたい。
https://qiita.com/kaizen_nagoya/items/088c57d70ab6904ebb53

官公庁・学校・公的団体（NPOを含む）システムの課題、官（０）
https://qiita.com/kaizen_nagoya/items/04ee6eaf7ec13d3af4c3

「はじめての」シリーズ　ベクタージャパン　
https://qiita.com/kaizen_nagoya/items/2e41634f6e21a3cf74eb

AUTOSAR(0)Qiita記事一覧, OSEK(75)
https://qiita.com/kaizen_nagoya/items/89c07961b59a8754c869

プログラマが知っていると良い「公序良俗」
https://qiita.com/kaizen_nagoya/items/9fe7c0dfac2fbd77a945

LaTeX(0) 一覧　
https://qiita.com/kaizen_nagoya/items/e3f7dafacab58c499792

自動制御、制御工学一覧（０）
https://qiita.com/kaizen_nagoya/items/7767a4e19a6ae1479e6b

Rust(0) 一覧　
https://qiita.com/kaizen_nagoya/items/5e8bb080ba6ca0281927

100以上いいねをいただいた記事16選
https://qiita.com/kaizen_nagoya/items/f8d958d9084ffbd15d2a

小川清最終講義、最終講義（再）計画, Ethernet(100) 英語(100) 安全(100)
https://qiita.com/kaizen_nagoya/items/e2df642e3951e35e6a53

＜この記事は個人の過去の経験に基づく個人の感想です。現在所属する組織、業務とは関係がありません。＞
This article is an individual impression based on my individual experience. It has nothing to do with the organization or business to which I currently belong.

文書履歴(document history)

ver. 0.01 初稿　20240808

最後までおよみいただきありがとうございました。

いいね　💚、フォローをお願いします。

Thank you very much for reading to the last sentence.

Please press the like icon 💚　and follow me for your happy life.