Document Type

Thesis

Date of Award

5-31-2021

Degree Name

Master of Science in Data Science - (M.S.)

Department

Computer Science

First Advisor

Chase Qishi Wu

Second Advisor

Jason T. L. Wang

Third Advisor

Senjuti Basu Roy

Abstract

Weather forecasting is a vital application in present times. We can use the predictions to minimize the weather related loss. Use of machine learning and deep learning algorithms for forecasting, can eliminate or reduce the necessity of big data and high computation dependent process of parameterization. Long Short-Term Memory (LSTM) is a widely used deep learning architecture for time series forecasting. In this paper, we aim to predict one day ahead average temperature using a 2-layer neural network consisting of one layer of LSTM and one layer of 1D convolution. The input is pre-processed using a smoothing technique and output is raw (un-smooth) next day average temperature. The smoothing technique improves the performance of LSTM substantially and meanwhile 1D convolution helps unsmooth the output of LSTM to obtain the raw answers. All the models are for particular locations only. The study shows significant improvement in the forecasting with use of smoothing technique. Our method outperforms other model in terms of MSE and MAE.

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Data Science Commons

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