## 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…

## Clean your Time Series data I: Missing values and detrending

Time series data is a sequence of observations recorded at regular time intervals and is commonly used in various fields such as finance, economics, and engineering. However, time series data can often contain noise, outliers, missing values, and other anomalies that can affect the analysis and interpretation of the data. Read more…

## Exogenous variables in ARIMA models

ARIMA models are very powerful for forecasting time series data when this data is univariate. However, there is a type of ARIMA model that can also consider other variables. This type of model is called ARIMAX, which stands for “Auto-Regressive Integrated Moving Average with eXogenous variables”. ARIMAX is an extension Read more…

## Forecasting methods in Time Series

When working with time series data, it is important to assess the quality of a model in a way that accurately reflects real-world situations. Generally, the simplified process of building a Machine Learning model is the following: This is the way it is traditionally done in Data Science. However, when Read more…

## ARIMA-GARCH models

ARCH/GARCH models can assist or improve the forecast of ARIMA models. Firstly, time series data must be stationary for these models to be applicable. If not, we should apply some transformations such as differencing. Since these models are mainly used in finance we will refer to the price returns from Read more…

## ARCH / GARCH models for Time Series

Volatility is a statistical measure of the dispersion of data or variance around its mean over a certain period of time. In finance, it refers to how much the price changes between periods. For example, if it is high, the price may increase or decrease a lot from one day Read more…