## Step-by-Step Guide to Time Series Forecasting with SARIMA Models

We have talked about ARIMA and SARIMA models previously, however, we have never shown a real case step by step. Let’s first recap, to make sure we know what an ARIMA model is. ARIMA model ARIMA (Auto-Regressive Integrated Moving Average) is a popular time series forecasting model that combines autoregressive Read more…

## Granger Causality in Time Series Forecasting

We talked about Vector Autorregression or VAR in a previous article. But, does it really make sense to use two different variables to get a forecast? The answer is no, not always at least. It will only be beneficial if there is some kind of relationship between them. Using unrelated Read more…

## Time Series Forecasting with Facebook Prophet III

In the previous part of our Facebook Prophet series, we covered how to model the seasonality component. You should also recall the first part, in which we dealt with trend modelling. In this article, we’ll focus on how we can add exogenous variables to our model. They are also known Read more…

## Theoretical Introduction to VAR Models for Time Series Forecasting

There are times when we need to forecast several variables at the same time. For these occasions, traditional methods such as ARIMA or Exponential Smoothing are not sufficient since they are univariate methods. Vector AutoRegression (VAR) is a statistical model for multivariate time series analysis and forecasting. It is used Read more…

## Time Series Forecasting with Facebook Prophet II

In the previous part of our Facebook Prophet series, we covered how to model the trend component and adjust the changepoints and regularization to improve forecasting accuracy. In this article, we’ll focus on the seasonal component and explore how to effectively model it using Facebook Prophet. The way Facebook Prophet Read more…

## Clean your Time Series data IV: Data Smoothing

In the first three parts of this series, we emphasized the significance of cleaning time series data and provided various techniques for handling missing data, dealing with duplicate values, and removing anomalies or outliers. In this final instalment, we will delve into data smoothing, which is a critical step in Read more…

## Clean your Time Series data III: Outliers removal

In the first two parts of this series, we discussed the importance of cleaning time series data and covered techniques for handling missing data and dealing with duplicate values. In this third, we’ll delve into an important aspect of cleaning time series data: outlier removal. Outliers are data points that Read more…

## Time Series Forecasting with Facebook Prophet I

Part I: Trend modeling Classical time series forecasting techniques rely on statistical models that require a significant amount of effort to fine-tune and tailor to specific industry data. This often involves adjusting parameters to ensure accurate performance, which requires in-depth knowledge of the underlying models. Prophet is an open-source library developed Read more…

## Exponential Smoothing for Time Series Forecasting

Exponential Smoothing is a popular time series forecasting method used for univariate data. While other methods, such as ARIMA models, develop a model based on a weighted linear sum of recent past observations or lags, Exponential Smoothing models employ an exponentially decreasing weight for past observations. This weight is calculated Read more…

## Clean your Time Series data II: Remove seasonality and normalize data

In the first part of this series, we saw that cleaning the data is an essential step in the time series analysis process. This consists of the following substeps: The article will provide a general guideline for removing the seasonality and normalizing the time series data. Please remember that depending Read more…