Time Series Forecasting: A beginner’s guide

Time series forecasting is a popular technique used in a wide range of fields such as finance, economics, weather forecasting, and many others. It is a method of predicting future values based on historical data, using statistical and machine learning models. Time series forecast is particularly useful for businesses to plan their future operations, anticipate changes in demand, and optimise resources.

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In this blog post, we will discuss this time series forecasting model and some popular algorithms used for time series forecasting.

Steps involved in building a time series forecasting model:

Data collection: the first step in building a time series prediction model is to collect historical data. This can be done using various sources such as government databases, company records, or online data repositories.

Data preparation: once the data is collected, it needs to be cleaned and prepared for analysis. This includes removing any missing or incorrect data points, converting the data into a uniform format, and identifying any outliers or anomalies.

Exploratory data analysis: after preparing the data, the next step is to perform exploratory data analysis to understand the underlying patterns and trends in the data. This involves creating visualisations, computing statistical measures, and identifying any season ability or trends.

Model selection: based on the analysis, the next step is to select an appropriate model for time series forecasting. This can range from simple statistical models such as ARIMA (Autoregressive Integrated Moving Average) to more complex machine learning models such as LSTM (Long Short-term Memory).

Model training and validation: once the model is selected, it needs to be trained on historical data and validated using a separate dataset. This involves tuning the model parameters and evaluating its performance based on metrics such as Mean Absolute Square Error(RMSE).

Model deployment and monitoring: After the model is trained and validated, it can be deployed to make predictions on new data. It is important to monitor the model performance regularly and retain or update the model as needed.

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Popular algorithm for time series forecasting:

ARIMA (Auto Integrated Moving Average): ARIMA is a statistical model used for time series forecasting. It combines auto regression, moving average, and differencing techniques to capture the underlying patterns in the data.

LSTM (Long Short-Term Memory): LSTM is a type of recurrent neural network used for time series analysis tools. It is particularly useful for capturing long-term dependencies in the data and can be used for tasks such as language translation and speech recognition.

Prophet: prophet is a forecasting tool developed by Facebook for time series data. It is a decomposable time series model and incorporates seasonality, holidays, and trends into the forecast.

SARIMA (Seasonal Autoregressive Integrated Moving Average): SARIMA is an extension of ARIMA that incorporates seasonality into the model. It is particularly useful for data with clear seasonal patterns such as sales data.

XGBoost (Extreme Gradient Boosting): XGBoost is a machine learning algorithm that can be used for time series prediction. It uses an ensemble of decision trees and is particularly useful for data with complex interactions and non-linear relationships.


In conclusion, time series prediction is an important tool for businesses and researchers to predict future trends and plan accordingly. By following the steps outlined above and using appropriate algorithms, it is possible to build accurate and reliable time series forecasting models.

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