EDA of stocks dataset and stocks prediction with ARIMA and non-linear models
Bosco, Leonardo (A.A. 2021/2022) EDA of stocks dataset and stocks prediction with ARIMA and non-linear models. Tesi di Laurea in Data analysis for business, Luiss Guido Carli, relatore Francesco Iafrate, pp. 22. [Bachelor's Degree Thesis]
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Abstract/Index
Exploratory data analysis. Presentation of the chosen dataset. Computation of volatility. Linear models. ARIMA model. Description of ARIMA. Data preparation. Arima (1,0,0). Forecasting. KNN algorithm on time series. Description of KNN algorithm. KNN on time series. Data preparation. One-step prediction. “MIMO and recursive” methods for multi-step ahead predictions. Measuring accuracy and plotting. Random forest algorithm on time series. Description of “decision tree” and “random forest” algorithms. “Walk forward validation” method. Multi-step ahead forecasting. Measuring accuracy. Plotting prediction test and test set and test set.
References
Bibliografia e sitografia: p. 21.
| Thesis Type: | Bachelor's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Bachelor's Degree Programs > Bachelor's Degree Program in Management and Computer Science, English language (L-18) |
| Chair: | Data analysis for business |
| Thesis Supervisor: | Iafrate, Francesco |
| Academic Year: | 2021/2022 |
| Session: | Autumn |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 21 Apr 2023 10:43 |
| Last Modified: | 21 Apr 2023 10:43 |
| URI: | https://tesi.luiss.it/id/eprint/35702 |
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