Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Modelling and Forecasting Bus Passenger Demand using Time Series Method
    (Institute of Electrical and Electronics Engineers Inc., 2018) Cyril, A.; Mulangi, R.H.; George, V.
    Public bus transport demand modelling and forecasting is important for decision-making, transport policy formulation, urban public transport planning and allocation of buses into the network. It is the key to the solutions for major transportation problems. In this paper, a univariate time series ARIMA model is used to forecast the inter-district public transport travel demand from Trivandrum to five other districts of Kerala. The data used in the study is a part of the transaction on ticket sales by Kerala State Road Transport Corporation (KSRTC) maintained at the Trivandrum central depot for the period between 2010 and 2013. ARIMA model is developed to predict the travel demand between the five district pairs and the demand is forecasted for future. The accuracy of the developed ARIMA model is demonstrated in the study by comparing the forecasted values with the actual demand observed in 2013. The results show that time series ARIMA model, which uses only historical data of passenger demand is accurate for zones which are dependent on each other and for short-term demand forecasting. © 2018 IEEE.
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    Stock Market Prediction Using Historical Stock Prices And Dependence On Other Companies In Automotive Sector
    (Institute of Electrical and Electronics Engineers Inc., 2022) Sharma, N.; Mohan, B.R.
    Stock market investment, due to its volatile nature and dependence on many factors like own company policies, dependence on other companies' stock value, people's outlook on the company, etc., is a big gamble. However, if understood, it can heap in big rewards to investors. This is one of the reasons why stock market analysis has been such a hot topic and a highly researched field. Fundamental and Technical analysis are two ways to study and predict future company stocks. A lot of work has been done previously to predict stock prices using either sentiment analysis or historical stock data, but a very little emphasis has been put on combining multiple factors to predict stock prices. In this study, we will work on companies registered in the automotive sector in NSE. We have focused on historical companies' stock details and the dependence of stock price of one company on other companies in the same sector to predict future stocks. Both of these factors were studied and analyzed, and then a comparative analysis was done to see which model better predicts the closing stock price of Tata Motors, our target company. We have used Autoregressive integrated moving average, Artificial Neural Network, Long Short-Term Memory (LSTM), a type of Recurrent Neural Network models in our research and a comparative analysis among them will be done. © 2022 IEEE.
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    Evaluation of ARIMA, Facebook Prophet and a boosting algorithm framework for monthly precipitation prediction of a semi-arid district of north Karnataka, India
    (Institute of Electrical and Electronics Engineers Inc., 2022) Chowdari, K.K.; Deb Barma, S.D.; Bhat, N.; Girisha, R.; Gouda, K.C.
    This study evaluates ARIMA, Facebook Prophet and a new boosting algorithm framework known as ThymeBoost for time series prediction of monthly precipitation of Belagavi district (semi-arid) in Karnataka. The dataset was divided into three periods (1901-2002, 1951- 2002, and 1971 - 2002). The first 70% of the data for each period was applied for training while the rest for testing. Also, the datasets were used in two different forms for both training and testing. In the first set, raw data was used as it is, and the second set of data was used after normalizing the time series using the min-max concept (between 0 and 1). However, the normalized data were de-normalized for each period for performance metrics estimation. ThymeBoost is the best model for the first period of raw data and the second period of normalized data. In contrast, Prophet outperforms all other models for the normalized data in terms of all four measures. For the second period of raw data, no model emerged as the best model in terms of all performance metrics. Therefore, all three models performed similarly for the third period of raw and normalized data. © 2022 IEEE.