Few businesses were shut down forever, others have seen a tremendous rise in usage.Time series forecasting models were put to test.

Time series forecasting is one of the predicting method based on historical data. Time series is a collection of data points collected at constant time intervals . Time series analysis is used in most fields where temporal measurements are made such as stock market, house price, weather, sales etc

This Blog is based on my talk at the International Symposium on Forecasting ISF2020, titled — Best Practices for Scaling Sales Forecasting.

This article provides an overview of the crop insurance program in the US provided by the Federal Crop Insurance Corporation via a network of private firms. We show that the Weibull distribution provides a reasonable option to model the loss payments between 0% and 100% of the liability levels, a finding consistent with prior research on insurance claims modeling.

In this article, I do not wish to go into the nitty-gritty of mathematical model selection and implementation. However, a high-level understanding of the project management framework used to execute the project is outlined below.

Forecast Error Measures: Scaled, Relative, and other Errors. Following through from my previous blog about the standard Absolute, Squared and Percent Errors, let’s take a look at the alternatives — Scaled, Relative and other Error measures for Time Series Forecasting.

Time Series data is one of the fastest-growing data out there and that is why it is imperative to have a good understanding of it. I will be using a dummy Sale information dataset over the course of two years. Let’s get started

In this article, we’ll do a simple sales forecast model and then blend external variables (properly done). So we’ll use the same model and we won’t do data wrangling or engineering at any point, so that we can tell apart only the benefit of adding useful features.

Welcome to the final part of my 3-blog series on building a predictive excellence engine. We will give a brief introduction to each along with details on how to implement them in python. In a separate blog we will discuss the best practices on optimizing each of these models.

We will be focusing on a step-by-step guideline that walks through the EDA and data cleaning process one can follow while working with multivariate time series data.

Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model. In this post I show you how to predict stock prices using a forecasting LSTM model

Identifying and Solving Glaring Chicago 311 Service Issues with Data Science. For this particular project, I decided to work with Chicago 311 service data.

A Complete Introduction To Time Series Analysis (with R):: Estimation of mu (mean). In this article, we consider the problem of estimating the trend, along with its statistical properties. “Why bother?” — you may ask.

In this article, we will explore the different types of features which are commonly engineered during forecasting projects and the rationale for using them.

In this post I show you how to predict stock prices using a forecasting model publicly available from Facebook Data Science team: The Prophet

Time-Series Forecasting: Predicting Stock Prices Using An ARIMA Model. In this post I show you how to predict the TESLA stock price using a forecasting ARIMA model

Create Forecast using Python — Prophet. This is multi-part series on how-to create a forecast, using one of the most widely used data science tool — Python.

For this project, I will make a model that will show long term flooding risk in an area. Related to climate change and machine learning, which I have been writing a lot about recently. The idea was to predict if an area has a higher risk of flooding in 10 years. The general idea to work this out was to get rainfall data. Then work out if the rainfall exceeded land elevation. After that, the area can be counted as flooded or not. Predicting Flooding with Python

Data Science for Business Users. Forecasting Part 2.1 — Create Forecast using Python — ARIMA

Python has great packages for training both ARIMA and GARCH models separately, but none that actually combine both (like R’s nifty package rugarch — damn you R users).