Predicting Weather using CNN-LSTM

The proposed system will be able to predict the crop yield production which will be useful to farmers for harvesting and storage. The system will use the weather forecasting which includes the parameters like temperature, rainfall, humidity, dew point and the normalized difference vegetation index time series from Sentinel-2 satellite for selected region. By using both the results obtained,accurate crop yield prediction can be calculated which will help farmers in planning efficiently, minimize costs and maximize yields—and profits—as a result.

Solar Energy prediction is a challenging problem, as it depends on the weather parameters of that region. The daily prediction of the solar energy of a solar farm is predicted from the historical daily production of the solar energy from the solar farm. This can be accomplished by time series forecasting technique, that predicts future events by analysing trends from the past, assuming that future trends will hold similar to historical trends. The Long Short-Term Memory (LSTM) is a variant of the Recurrent Neural Networks (RNN) that is capable of learning long term dependencies in the data. This project presents the prediction of Solar Energy with time series LSTM.

Multivariate chaotic time series prediction, a popular research topic which is concerned with many disciplines (weather forecasting and predicting stocks), where the end goal is to predict the future of the time series based on past observations. Various neural networks has been proposed to forecast future values in time series data but existing methods are not comprehensively evaluated.

1> Background information Bluebikes is Metro Boston’s public bike share program, with more than 1800 bikes at over 200 stations across Boston and nearby areas. The bikes sharing program launched in 2011. The program aimed for individuals to use it for short-term basis for a price. It allows individuals to borrow a bike from a dock station after using it, which makes it ideal for one-way trips. The City of Boston is committed to providing bike share as a part of the public transportation system. However, to build a transport system that encourages bicycling, it is important to build knowledge about the current bicycle flows, and what factors are involved in the decision-making of potential bicyclists when choosing whether to use the bicycle. It is logical to make hypotheses that age and gender, bicycle infrastructure, safety perception are possible determinants of bicycling. On the short-term perspective, it has been shown that weather plays an important role whether to choose the bicycle. 2> Data collection The Bluebikes collects and provides system data to the public. The datasets used in the project can be download through this link (https://www.bluebikes.com/system-data). Based on this time series dataset (start from 2017-01-01 00:00:00 to 2019-03-31 23:00:00), we could have the information includes: Trip duration, start time and data, stop time and data, start station name and id, end station name and id, bike id, user type (casual or subscribed), birth year, gender. Besides, any trips that were below 60 seconds in length is considered as potentially false starts, which is already removed in the datasets. The number of bicycles used during a particular time period, varies over time based on several factors, including the current weather conditions, time of the day, time of the year and the current interest of the biker to use the bicycle as a transport mode. The current interest is different between subscribed users and casual users, so we should analyze them separately. Factors such as season, day of a week, month, hour, and if a holiday can be extracted from the date and time column in the datasets. Since we would analyze the hourly bicycle rental flow, we need hourly weather conditions data from 2017-01-01 00:00:00 to 2019-03-31 23:00:00 to complete our regression model of prediction. The weather data used in the project is scrapped using python selenium from Logan airport station (42.38 °N, 71.04 °W) webpage (https://www.wunderground.com/history/daily/us/ma/boston/KBOS/date/2019-7-15) maintained by weather underground website. The hourly weather observations include time, temperature, dew point, humidity, wind, wind speed, wind gust, pressure, precipitation, precipitation accumulated, condition. 3> The problem The aims of the project are to gain insight of the factors that could give short-term perspective of bicycle flows in Boston. It also aimed to investigate the how busy each station is, the division of bicycle trip direction and duration of the usage of a busy station and the mean flows variation within a day or during that period. The addition to the factors included in the regression model, there also exist other factors than influence how the bicycle flows vary over longer periods time. For example, general tendency to use the bicycle. Therefore, there is potential to improve the regression model accuracy by incorporating a long-term trend estimate taken over the time series of bicycle usage. Then the result from the machine learning algorithm-based regression model should be compared with the time series forecasting-based models. 4> Possible solutions Data preprocessing/Exploration and variable selection: date approximation manipulation, correlation analysis among variables, merging data, scrubbing for duplicate data, verifying errors, interpolation for missing values, handling outliers and skewness, binning low frequent levels, encoding categorical variables. Data visualization: split number of bike usage by subscribed/casual to build time series; build heatmap to present how busy is each station and locate the busiest station in the busiest period of a busy day; using boxplot and histogram to check outliers and determine appropriate data transformation, using weather condition text to build word cloud. Time series trend curve estimates: two possible way we considered are fitting polynomials of various degrees to the data points in the time series or by using time series decomposition functions and forecast functions to extract and forecast. We would emphasize on the importance to generate trend curve estimates that do not follow the seasonal variations: the seasonal variations should be captured explicitly by the input weather related variables in the regression model. Prediction/regression/time series forecasting: It is possible to build up multilayer perceptron neural network regressor to build up models and give prediction based on all variables of data, time and weather. However, considering the interpretability of model, we prefer to build regression models based on machine learning algorithms (like random forest or SVM) respectively for subscribed/casual users. Then the regressor would be combined with trend curve extracted and forecasted by ARIMA, and then comparing with the result of time series forecasting by STL (Seasonal and Trend decomposition using Loess) with multiple seasonal periods and the result of TBATS (Trigonometric Seasonal, Box-Cox Transformation, ARMA residuals, Trend and Seasonality).

Amount of Rainfall prediction is a major issue for the weather department as it is associated with the human’s life and economy. Rainfall is the major cause of natural disasters such as drought and flood which are encountered by the people every year across the world. We report machine learning model for the time-series analysis and forecasting of rainfall at TamilNadu. The Indian meteorological department provides forecasting data required for project.Seasonal ARIMA is the technique applied to model the monthly rainfall at TamilNadu for the duration from January 1990 to December 2017. Using SARIMA model proved the stationarity of the time series flow and evaluated the seasonal correlogram

• Weather Forecasting is the process of making predictions of the future, based on past and present data of the weather. • We used ARIMA model(Auto Regressive Integrated Moving Average) to analyze and predict the time-series data and we shall also perform rigorous exploratory data analysis and visualizations on the dataset. • Feature Engineering – selecting required attributes. • Data cleaning – renaming attributes and filling missing data. • Check rolling mean and standard deviation (graph must not vary too much for stationarity). • Perform Augmented Dickey–Fuller test (to check for stationarity) • plotting PACF(partial auto correlation function) and ACF(auto correlation function) to find p and q values of ARIMA model. • Fitting and forecasting the model for temperature data. • This could be also be used other types of time series data such as stock prices, market price variations, etc.

Time Series Forecasting Experiments A collection of hands-on experiments with time series data, featuring models like ARIMA, LSTM, and Prophet. From data preprocessing to forecasting, explore real-world applications like stock predictions and weather forecasting. Continuously updated with new techniques and models for better performance.

A full stack SARIMA/SVAR weather forecasting application done entirely within AWS cloud computing architecture (EC2, RDS, S3) using Boto3, and PyMySQL. I compare SARIMA and Seasonal VAR time series models as weather forecasters and host predictions and performance on a weather dashboard website

A deep learning framework for time series forecasting using attention mechanisms. This repository provides an implementation of state-of-the-art neural architectures designed for accurate and interpretable time series prediction tasks such as energy consumption, stock price, weather data, and more.

Time Series Forecasting is a technique in data analysis and machine learning that predicts future values based on previously observed sequential data. It is widely used in finance, weather, sales, and demand prediction to identify trends, seasonality, and patterns over time.

A professional, full-stack web application for forecasting power consumption using advanced ARIMA and SARIMAX time series forecasting models. This system provides accurate predictions based on weather-related features and offers a modern, responsive web interface for real-time forecasting.

The Future Temperature Prediction using LSTM project predicts upcoming temperatures based on historical weather data. Using an LSTM neural network, it captures temporal patterns and seasonal trends in time-series data. The model helps in accurate forecasting, climate analysis with deep learning efficiency.

Predicting weather using SARIMA model of Time Series Forecasting

Project on Time Series Forecasting to make Climate Change Predictions. An ML functional model based on SARIMA model and JAX.

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🌦️ ForeCast360: Weather prediction and forecasting system using time-series deep learning, live weather data, and an interactive dashboard for accurate, accessible forecasts.

End-to-end weather forecasting system using time-series analysis, feature engineering, and LSTM-based prediction models.

This project showcases practical applications of time series forecasting and logistic regression for weather prediction and cardiac health analysis.

Time series forecasting on weather data using AR, RNN, Attention, and Transformer models across multiple resolutions and prediction horizons.

Time series forecasting is essential in domains such as energy consumption, finance, traffic analysis, weather modeling, and IoT sensor prediction.

Utilize machine learning and time series analysis to make state-wise electricity demand prediction based on weather forecast

This repository serves as a comprehensive resource for time series forecasting using non-linear machine learning (NLLM) models. Time series forecasting is a crucial task across various domains such as finance, economics, weather prediction, and more.

Comprehensive analysis of global weather data: covering data cleaning, EDA, time series forecasting (ARIMA, ES, Prophet, Ensemble), feature importance, and spatial analysis for temperature prediction.

A time series forecasting and visualization system based on Qwen and LSTM models, supporting multi-task prediction on weather, electricity consumption, and traffic flow data.

Time series forecasting is the task of predicting future data points, given some historical data. It is commonly used in a variety of tasks from weather forecasting, retail and sales forecasting, stock market prediction, and in behavior prediction (such as predicting the flow of car traffic over a day).

AI/ML-based Crop Yield Prediction system using weather data and NDVI time-series to forecast agricultural productivity and support data-driven farming decisions.

Time series forecasting is the process of analyzing time series data using statistics and modeling to make predictions and inform strategic decision-making. We are using Tensor-Flow to analyse weather periodic data contains 14 different features such as air temperature, atmospheric pressure, and humidity. we tried 5 differet models to analyse and forcast time series data of weather.

The Weather Prediction project aims to develop a machine learning-based system capable of forecasting future weather conditions such as temperature, humidity, rainfall, and wind speed using historical weather data. The project leverages supervised learning algorithms and time-series analysis to build accurate and reliable predictive models.

This project develops a time series forecasting model to predict energy production and consumption for prosumers. It uses weather data, installed capacity, and other relevant features to enhance prediction accuracy, helping prosumers optimize their energy management