| seminars |
| filter: |
 list |  browse |
 id : |
1111
|
| type : |
MSc_Thesis
|
| dateandtime : |
2024-04-29 13:30:00
|
| duration : |
90 min.
|
|
Recommended duration for PhD thesis is 90 minutes, for other seminar types, it is 60 minutes. The duration specified here is used to reserve the room.
|
| place : |
A105
|
|
Please check room availability from Room Scheduling page. You must use the same room name as used in the scheduling page if you want to automatically reserve the room.
|
| departmental : |
yes
|
| title : |
ADAPTIVE INCREMENTAL LEARNING FOR STOCK TREND
FORECASTING
|
| author : |
LEYLA HELIN CETIN DELIKAYA
|
| supervisors : |
PROF.DR.M.VOLKAN ATALAY
|
|
Supervisors field is applicable especially for a Thesis Defense
|
| company : |
Computer Engineering Dept. Middle East Technical Univ.
|
| country : |
Turkey
|
| abstract : |
Batch learning approaches cannot cope with the concept drift inherent in the stock market stream data. On the other hand, incremental learning has not been fully explored as a solution to the problem of concept drift in the stock market stream data. We propose ADIN-Forecast that detects changes in stock features and quickly adapts the model structure to mitigate the drawbacks of data shifts through incremental learning. ADIN-Forecast uses a Gated Recurrent Unit (GRU), enabling the self-growth of layers and hidden units that can dynamically adjust to the changes in the data distribution. Self-growing model architectures are known to experience catastrophic forgetting. To counter this, we have developed a control mechanism capable of activating or deactivating layers and applying a penalty coefficient to the layers’ weights depending on the occurrence of concept drifts. Forecasting model GRU in ADIN-Forecast can also be substituted with other neural networks, such as MLP, RNN, and LSTM. ADIN-Forecast uses the difference between the PCA eigenvectors for the two consecutive data windows to detect changes and offers a model that evolves dynamically according to these changes while ensuring memory and time efficiency through its incremental nature. We evaluated our methodology on the CSI 300 dataset in the open-source quantitative investment platform Qlib and compared it with other studies in the field.
|
| biography : |
|
| download slides : |
|
|
[ ]check this to delete slides
|
| slidesFilename : |
|
|
slidesFilename is the name of downloadable file, and will be automatically filled when you upload a new file. You may change the name also.
|
| links : |
|
| notificationSent : |
final
|
