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Browsing by Subject "time series forecasting"

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  • De-centralized Learning for Radio Network Key Performance Indicator Prediction 
    Alcantara, Jose Carlos (2020)
    A recent machine learning technique called federated learning (Konecny, McMahan, et. al., 2016) offers a new paradigm for distributed learning. It consists of performing machine learning on multiple edge devices and simultaneously optimizing a global model for all of them, without transmitting user data. The goal for this thesis was to prove the benefits of applying federated learning to forecasting telecom key performance indicator (KPI) values from radio network cells. After performing experiments with different data sources' aggregations and comparing against a centralized learning model, the results revealed that a federated model can shorten the training time for modelling new radio cells. Moreover, the amount of transferred data to a central server is minimized drastically while keeping equivalent performance to a traditional centralized model. These experiments were performed with multi-layer perceptron as model architecture after comparing its performance against LSTM. Both, input and output data were sequences of KPI values.
  • Forecasting the popularity of TikTok hashtags from time series data 
    Bovellán, Jonne (2022)
    A lot of research is done in terms of time series forecasting. This research is conducted for example on the stock market data and the data derived from social media platforms. Several powerful methods are developed for time-series forecasting, including Bidirectional Long Short-Term Memory which is based on neural networks. TikTok is a social media platform that is focused on short videos. When a user posts a video to TikTok, they can also write a short textual description which can include hashtags. Often these hashtags describe things, events and trends that happen in the physical world. The possibility to have an accurate forecast of the future popularity of TikTok hashtags includes the financial potential it creates for individuals and organisations. As part of this thesis, an experimental study was conducted in order to forecast the popularity of TikTok hashtags. An algorithm based on Bidirectional Long-Short Term Memory was created that forecasts short-term and long-term popularity of a single hashtag based on its past. A data set which consisted of time series data for 9779 different TikTok hashtags was used in the development process. The created forecasting algorithm performs at a good level for forecasting the short-term popularity of a hashtag, but it is not suitable for long-term forecasting due to its bad performance.

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