North Africa, Wood Processing Equipment, Export Market, 1000 EUR, Value (Y)
Value (Y) Time Available: 2002 - 2030
Data Summary: North Africa, Wood Processing Equipment, Export Market, 1000 EUR, Trend + Forecast, Value (Y)
Time series data for North Africa, Wood Processing Equipment, Export Market, 1000 EUR, Trend + Forecast, Value (Y)
Time Period
Value
Data Range
2015
81.220,57
2002 - 2030
2016
68.107,70
2002 - 2030
2017
78.350,11
2002 - 2030
2018
89.106,71
2002 - 2030
2019
83.668,31
2002 - 2030
2020
110.186,22
2002 - 2030
2021
155.057,36
2002 - 2030
2022
68.557,72
2002 - 2030
2023
85.908,31
2002 - 2030
2024
177.341,37
2002 - 2030
2025
111.767,13
2002 - 2030
This dataset contains 11 data points spanning from 2015 to 2025.
Analysis dimensions: Region, Product, Measure, Unit, Analysis.
Statistical summary: Minimum value 68,107.70, Maximum value 177,341.37, Average value 100,842.87.
The chart above illustrates a time series model generated by the Innomis Market Management Platform - one of many analytic models and visualization options available.
To discover how Innomis continuously delivers and transforms millions of data records into efficient information and actionable intelligence - using digital assistants and AI for informed decision-making in market management - please contact us to schedule a personal demo.
Metadata
Solution
Export Market Intelligence
Region
North Africa
Product
Wood Processing Equipment
Measure
Export Market
Unit
1000 EUR
Analysis
Value (Y)
TIME (Displayed)
2015 - 2025
TIME (Available)
2002 - 2030
Data Insights and Analysis
This comprehensive dataset spans 11 time periods from 2015 to 2025. The analysis reveals values ranging from 68,107.70 to 177,341.37, with an average value of 100,842.87.
The data shows an overall upward trend with a total change of 30,546.56 (37.6% increase) over the analyzed period.
Key analytical dimensions include: North Africa, Wood Processing Equipment, Export Market, 1000 EUR, Trend + Forecast, Value (Y). This multi-dimensional analysis provides comprehensive insights into the underlying data patterns and trends.
