Time-Series-Forecasting

Overview

Time series forecasting involves building models through historical analysis and using them to make observations and drive future strategic decision-making. When we associate a temporal or time component to the forecast, it becomes Time Series Forecasting and the data is called as Time Series Data.

Definition

In statistical terms, time series forecasting is the process of analyzing the time series data using statistics and modeling to make predictions and informed strategic decisions. It falls under Quantitative Forecasting.

Components

To use time-series data and develop a model, you need to understand the patterns in the data over time. These patterns are classified into four components, which are:

Trend: It represents the gradual change in the time series data. The trend pattern depicts long-term growth or decline.

Level: It refers to the baseline values for the series data if it were a straight line

Seasonality: It represents the short-term patterns that occur within a single unit of time and repeats indefinitely.

Noise: It represents irregular variations and is purely random. These fluctuations are unforeseen, unpredictable, and cannot be explained by the model

Applications

Time Series Forecasting can be used in:

Stock Price Prediction, E-Commerce Sales Prediction, Weather Predicition and many more

Zindi has hosted some challenges based on Time-Series Forecasting Solutions.

List of Time-Series Forecasting solutions from our hosted challenges:

1. AI4D Predict the Global Spread of COVID-19

2. AirQo Ugandan Air Quality Forecast Challenge

3. Mtoto News Childline Kenya Call Volume Prediction Challenge

4. Sea Turtle Rescue: Forecast Challenge

5. UNICEF Arm 2030 VISION #1: Flood Prediction in Malawi

6. Wazihub Soil Moisture Prediction Challenge