This article was published as a part of the Data Science Blogathon. This article will implement time series forecasting using the Prophet library in python. The prophet is a package that facilitates the simple implementation of time series analysis. Implementing time series forecasting can be complicated depending on the model we use. Many approaches are available for time series forecasting, for example, ARIMA ( Auto-Regressive Integrated Moving Average), Auto-Regressive Model, Exponential Smoothing, and deep learning-based models like LSTM ( long short term memory).

Artificial Intelligence is the root of both machine learning & deep learning, machine learning is a subset of artificial intelligence and deep learning is a subset of machine learning in that flow. Deep learning plays an important role in the advancement of artificial intelligence in many ways, using such an important feature for the prediction of data on daily basis gives better results and also helps in the understanding of various neglected sides. The cryptocurrency has been evolved and grown to a very large amount, estimating to a billion-dollar industry. Understanding such huge digital currency is difficult and also to estimate the change in trend is important, as a change in trend can lead to profit or loss of a particular cryptocurrency. The number of cryptocurrencies over the year has increased with new currency coming out, this introduction of digital currency can tell the demand of them in the market, due to the non-presence of such currency it becomes difficult to track the change, this is where deep learning would come in handy.

Air contamination in urban areas has risen consistently over the past few years. Due to expanding industrialization and increasing concentration of toxic gases in the climate, the air is getting more poisonous step by step at an alarming rate. Since the arrival of the Coronavirus pandemic, it is getting more critical to lessen air contamination to reduce its impact. The specialists and environmentalists are making a valiant effort to gauge air contamination levels. However, its genuinely unpredictable to mimic subatomic communication in the air, which brings about off base outcomes. There has been an ascent in using machine learning and deep learning models to foresee the results on time series data. This study adopts ARIMA, FBProphet, and deep learning models such as LSTM, 1D CNN, to estimate the concentration of PM2.5 in the environment. Our predicted results convey that all adopted methods give comparative outcomes in terms of average root mean squared error. However, the LSTM outperforms all other models with reference to mean absolute percentage error.

The series of data points plotted against time is known as time series. It is a de-facto analysis technique used in market evaluation and in weather forecast. It is an exciting topic to study as it somehow tends to predict the future, which we are always interested in. We can make forecast of tomorrow's weather by observing the weather of past few days. If the weather was sunny for last 4–5 days then there is high chance for weather to be sunny tomorrow.