Time Series Weather Forecasting Techniques: Literature Survey
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This document summarizes various time series forecasting techniques discussed in literature, including ARIMA, Prophet, and LSTM models. It reviews their applications in weather forecasting, analyzing COVID-19 data, real estate prices, bitcoin values, and more. The key techniques are compared based on their forecasting accuracy on different datasets. ARIMA is generally good at capturing trends but requires stationary data, while Prophet and LSTM can handle non-stationary data and seasonal effects better. Prophet achieved 91% accuracy on a COVID dataset, outperforming ARIMA. LSTM achieved 76% accuracy for rainfall forecasting. The document concludes different approaches are still being developed to address the unique challenges of weather data forecasting.
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Time Series Weather Forecasting Techniques: Literature Survey