Solving Some Problems of Predictive Analytics for Time Series Data; Lecture Notes in Networks and Systems; Vol. 501 : Software Engineering Perspectives in Systems
| Parent link: | Lecture Notes in Networks and Systems.— .— Cham: Springer Vol. 501 : Software Engineering Perspectives in Systems.— 2022.— P. 382-391 |
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| Zusammenfassung: | Title screen Approaches for constructing an additive regression model and a seasonally integrated autoregressive-moving average model were considered. Experiments were aimed at investigating and developing an algorithm for forecasting meteorological fields. In particular, a time series forecast of monthly precipitation totals for the next two years was built. In the software experiments for building forecast models, arrays containing data from eight-year observations of atmospheric precipitation were used. That is, sums of precipitation for the period between standard synoptic dates with an interval of three hours were used. The year 2016 was selected as the forecast year. The original data series was processed - emissions that were not attributed to seasonal variations were removed. Additionally, the allowable maximum amount of precipitation between dates for an observation station with synoptic index 29430 recommended by the All-Russian Research Institute for Hydrometeorological Information - World Data Centre (VNIIGMI-WDC) was controlled. The Python programming language was used as a tool to generate forecasts for the time series data. An additive regression model was created using the Prophet library from Facebook. A seasonally integrated autoregressive-moving average model was created using the StatsModels library. A DF-test (Dickey-Fuller basic test), which checks for the presence of a single “unit root”, was used to test for stationarity. The testing and writing of the program code took place in the interactive Jupyter Notebook environment. The environment is a graphical web shell for Python and extends the idea of the console approach to interactive computing. Model forecast accuracy was assessed by calculating absolute and mean absolute prediction errors Текстовый файл AM_Agreement |
| Sprache: | Englisch |
| Veröffentlicht: |
2022
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| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.1007/978-3-031-09070-7_32 |
| Format: | Elektronisch Buchkapitel |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=679997 |
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| 200 | 1 | |a Solving Some Problems of Predictive Analytics for Time Series Data |f Igor Botygin, Anna Sherstneva, Vladislav Sherstnev | |
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| 330 | |a Approaches for constructing an additive regression model and a seasonally integrated autoregressive-moving average model were considered. Experiments were aimed at investigating and developing an algorithm for forecasting meteorological fields. In particular, a time series forecast of monthly precipitation totals for the next two years was built. In the software experiments for building forecast models, arrays containing data from eight-year observations of atmospheric precipitation were used. That is, sums of precipitation for the period between standard synoptic dates with an interval of three hours were used. The year 2016 was selected as the forecast year. The original data series was processed - emissions that were not attributed to seasonal variations were removed. Additionally, the allowable maximum amount of precipitation between dates for an observation station with synoptic index 29430 recommended by the All-Russian Research Institute for Hydrometeorological Information - World Data Centre (VNIIGMI-WDC) was controlled. The Python programming language was used as a tool to generate forecasts for the time series data. An additive regression model was created using the Prophet library from Facebook. A seasonally integrated autoregressive-moving average model was created using the StatsModels library. A DF-test (Dickey-Fuller basic test), which checks for the presence of a single “unit root”, was used to test for stationarity. The testing and writing of the program code took place in the interactive Jupyter Notebook environment. The environment is a graphical web shell for Python and extends the idea of the console approach to interactive computing. Model forecast accuracy was assessed by calculating absolute and mean absolute prediction errors | ||
| 336 | |a Текстовый файл | ||
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| 461 | 1 | |t Lecture Notes in Networks and Systems |c Cham |n Springer | |
| 463 | 1 | |t Vol. 501 : Software Engineering Perspectives in Systems |o Proceedings of the 11th Computer Science Online Conference 2022 (CSOC2022) |i Vol. 1 |v P. 382-391 |d 2022 |y 978-3-031-09070-7 |y 978-3-031-09069-1 | |
| 610 | 1 | |a Regression analysis | |
| 610 | 1 | |a Additive regression model | |
| 610 | 1 | |a Autoregressive integrated moving average model | |
| 610 | 1 | |a электронный ресурс | |
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| 700 | 1 | |a Botygin |b I. A. |c specialist in the field of Informatics and computer engineering |c Associate Professor of Tomsk Polytechnic University, candidate of technical sciences |f 1947- |g Igor Aleksandrovich |9 17356 | |
| 701 | 1 | |a Sherstneva |b A. I. |c mathematician |c associate Professor of Tomsk Polytechnic University, candidate of physico-mathematical Sciences |f 1974- |g Anna Igorevna |9 18721 | |
| 701 | 1 | |a Sherstnev |b V. S. |c specialist in the field of Informatics and computer engineering |c associate Professor of Tomsk Polytechnic University, candidate of technical Sciences |f 1974- |g Vladislav Stanislavovich |9 17137 | |
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