GRA - Revision history

Jacandrade at 17:03, 5 August 2018

2018-08-05T17:03:52Z

Jacandrade at 17:01, 5 August 2018

2018-08-05T17:01:51Z

Jacandrade at 17:00, 5 August 2018

2018-08-05T17:00:51Z

Jacandrade at 16:56, 5 August 2018

2018-08-05T16:56:44Z

Jacandrade at 16:56, 5 August 2018

2018-08-05T16:56:14Z

Andrew at 17:47, 15 February 2018

2018-02-15T17:47:58Z

Andrew at 17:41, 15 February 2018

2018-02-15T17:41:31Z

Andrew at 17:36, 15 February 2018

2018-02-15T17:36:56Z

Jacandrade at 22:01, 1 February 2018

2018-02-01T22:01:59Z

Jacandrade at 22:01, 1 February 2018

2018-02-01T22:01:25Z

@@ Line 30: / Line 30: @@
 The input on the left allows the user to upload a file with the time series to be analysed. The file MUST be on a .CSV format to ensure compatibility with the forecasting algorithm, containing all the values in the first column, one value per row:
-<code>
+<code>value1</code>
-value2
+<code>value2</code>
-value3
+<code>value3</code>
-value4
+<code>value4</code>
-...
+<code>...</code>
 Each row represents a time point in the time series. You don't need to enter the month for the values, as each entry is considered a new point value for the month following its preceding value. The results will display the index value of each entry as ordered in the entry data in the X axis starting at 0, and the forecast results will be added as entries at the end of the time series original values.
@@ Line 52: / Line 50: @@
 The input on the left allows you to quickly create a forecast by pasting the values of a time series in the text box and using it as an input. Each value for the time series must be entered one after the other, separated by a semi-colon '';''. You will get an error message if any of the values are not in the correct format. Once everything is ready click the calculate button below the input to generate the forecast. You can copy and paste the example below in the tool for a demonstration:
-<code>2500;2750;1598;1777;2100;2107;2213;1993;1768;1479;1344;1389;1433;1477;1578;1611;1678;1587;1523;1124;1388;1299;1433;1399</code>
+<pre>2500;2750;1598;1777;2100;2107;2213;1993;1768;1479;1344;1389;1433;1477;1578;1611;1678;1587;1523;1124;1388;1299;1433;1399</pre>
 === The results ===

@@ Line 11: / Line 11: @@
 ----
-The tool analyses a time series of data of any value type such as profit per month,
+In the research phase of the project, simulation models were developed to analyse how changes in supply and demand affect production planning, economic performance, supply chain relationships, value added, potential product success, market trends and developments and thus competitiveness as measured by the updated [[CPA|FACI]]. One of the outcomes of the research is the Growth risk analyser tool, implemented through simulation/forecasting models for analysing and forecasting pricing trends in the short term: up to 12 months with a confidence band of 95% in the case of this tool. Forecasts given outside of 12 months become less reliable as the forecast horizon increases (up to 24 months are generated by the tool).
 == Tool Overview ==
@@ Line 29: / Line 22: @@
 ----
-===Landing page===
+=== Landing page ===
-The tool is very simple and it's homepage presents the users with two different forms of input for
+The tool is very simple and it's homepage presents the users with two different forms of input for the data to be analysed.
-[[File:Grahome.png| GRA home]]
+[[File:Grahome.png|GRA home]]
-The input on the left allows the user to upload a file with the time series to be analysed. The file MUST be on
+The input on the left allows the user to upload a file with the time series to be analysed. The file MUST be on a .CSV format to ensure compatibility with the forecasting algorithm, containing all the values in the first column, one value per row:
 <code>
 value1
 value2
-value3
+value2
 value4
 ...
 </code>
-Each row represents a time point in the time series. You don't need to enter the month for
+Each row represents a time point in the time series. You don't need to enter the month for the values, as each entry is considered a new point value for the month following its preceding value. The results will display the index value of each entry as ordered in the entry data in the X axis starting at 0, and the forecast results will be added as entries at the end of the time series original values.
-[[File:Sprdsheetvalues.png| time series in a spreadsheet]]
+You can create a .CSV file with any spreadsheet editor by simply creating a new spreadsheet and adding the values in the first column, one per row as in the image below:
-After that, click the '''file''' menu of your spreadsheet editor and select '''save as...'''.
+[[File:Sprdsheetvalues.png|time series in a spreadsheet]]
-The input on the left allows you to quickly create a forecast by pasting the values of a time series in the
+After that, click the '''file''' menu of your spreadsheet editor and select '''save as...'''. Once the dialog box for saving opens up, insert the name for the file and select the location. The last and most important and final step is to select '''Text CSV''' as a type in the '''format''' dropdown for the file type which is generally located above the save button. Once you selected the correct type, click the save button and the file will be ready to be uploaded. You can then proceed to the GRA tool home page and click the ''choose file'' button to select your saved file and then click the ''Upload file and calculate forecast'' button to generate your forecast. You can download an example .csv file [[File:Example values.csv|here]].
-<code>
+The input on the left allows you to quickly create a forecast by pasting the values of a time series in the text box and using it as an input. Each value for the time series must be entered one after the other, separated by a semi-colon '';''. You will get an error message if any of the values are not in the correct format. Once everything is ready click the calculate button below the input to generate the forecast. You can copy and paste the example below in the tool for a demonstration:
-===The results===
+<code>2500;2750;1598;1777;2100;2107;2213;1993;1768;1479;1344;1389;1433;1477;1578;1611;1678;1587;1523;1124;1388;1299;1433;1399</code>
-Once you click the forecast button you will be redirected to the results page where you will see on the
+=== The results ===
-[[File:Graresult1.png| GRA results page]]
+Once you click the forecast button you will be redirected to the results page where you will see on the left sidebar the current request status and the request input as interpreted by the algorithm once the calculations are complete. It may take a few seconds for the algorithm to run, but you should the see the result graphs.
-The first graph shows the input values and a fitted trend line for the approximation trend generated.
+[[File:Graresult1.png|GRA results page]] [[File:Graresult2.png|GRA results page]] [[File:Graresult3.png|GRA results page]]
 == References & Readings ==
 ----

@@ Line 11: / Line 11: @@
 ----
-In the research phase of the project, simulation models were developed to analyse how changes in supply and demand affect production planning, economic performance, supply chain relationships, value added, potential product success, market trends and developments and thus competitiveness as measured by the updated [[CPA|FACI]]. One of the outcomes of the research is the Growth risk analyser tool, implemented through simulation/forecasting models for analysing and forecasting pricing trends in the short term: up to 12 months with a confidence band of 95%
+The tool analyses a time series of data of any value type such as profit per month,
-Forecasts given outside of 12 months become less reliable as the forecast horizon increases (up to 24 months are generated by the tool).
+== Tool Overview ==
-The tool analyses a time series of data of any value type such as profit per month, average sales quantity per day/week/fortnight/etc, average catch/landings per month/semester/year/etc, containing a minimum of 24 values (to ensure accuracy).
+----
-&nbsp;
+===Landing page===
-== Tool Overview ==
+The tool is very simple and it's homepage presents the users with two different forms of input for
+the data to be analysed.
-=== Landing page ===
+[[File:Grahome.png| GRA home]]
-The tool is very simple and it's homepage presents the users with two different forms of input for the data to be analysed.
+The input on the left allows the user to upload a file with the time series to be analysed. The file MUST be on
-[[File:Grahome.png|GRA home]]
+<code>
-The input on the left allows the user to upload a file with the time series to be analysed. The file MUST be on a .CSV format to ensure compatibility with the forecasting algorithm, containing all the values in the first column, one value per row:
+Each row represents a time point in the time series. You don't need to enter the month for
-value1 value2 value3 value4 ...
+You can create a .CSV file with any spreadsheet editor by simply creating a new spreadsheet and
-Each row represents a time point in the time series. You don't need to enter the month for the values, as each entry is considered a new point value for the month following its preceding value. The results will display the index value of each entry as ordered in the entry data in the X axis starting at 0, and the forecast results will be added as entries at the end of the time series original values.
+[[File:Sprdsheetvalues.png| time series in a spreadsheet]]
-You can create a .CSV file with any spreadsheet editor by simply creating a new spreadsheet and adding the values in the first column, one per row as in the image below:
+After that, click the '''file''' menu of your spreadsheet editor and select '''save as...'''.
-[[File:Sprdsheetvalues.png|time series in a spreadsheet]]
+The input on the left allows you to quickly create a forecast by pasting the values of a time series in the
-After that, click the '''file''' menu of your spreadsheet editor and select '''save as...'''. Once the dialog box for saving opens up, insert the name for the file and select the location. The last and most important and final step is to select '''Text CSV''' as a type in the '''format''' dropdown for the file type which is generally located above the save button. Once you selected the correct type, click the save button and the file will be ready to be uploaded. You can then proceed to the GRA tool home page and click the ''choose file'' button to select your saved file and then click the ''Upload file and calculate forecast'' button to generate your forecast. You can download an example .csv file [[File:Example values.csv|here]].
+<code>
-The input on the left allows you to quickly create a forecast by pasting the values of a time series in the text box and using it as an input. Each value for the time series must be entered one after the other, separated by a semi-colon '';''. You will get an error message if any of the values are not in the correct format. Once everything is ready click the calculate button below the input to generate the forecast. You can copy and paste the example below in the tool for a demonstration: <code>2500;2750;1598;1777;2100;2107;2213;1993;1768;1479;1344;1389;1433;1477;1578;1611;1678;1587;1523;1124;1388;1299;1433;1399</code>
+===The results===
-=== The results ===
+Once you click the forecast button you will be redirected to the results page where you will see on the
-Once you click the forecast button you will be redirected to the results page where you will see on the left sidebar the current request status and the request input as interpreted by the algorithm once the calculations are complete. It may take a few seconds for the algorithm to run, but you should the see the result graphs.
+[[File:Graresult1.png| GRA results page]]
-[[File:Graresult1.png|GRA results page]] [[File:Graresult2.png|GRA results page]] [[File:Graresult3.png|GRA results page]]
+The first graph shows the input values and a fitted trend line for the approximation trend generated.
 == References & Readings ==
 ----

@@ Line 11: / Line 11: @@
 ----
 In the research phase of the project, simulation models were developed to analyse how changes in supply and demand
-affect production planning, economic performance, supply chain relationships, value added, potential product success,
+In the research phase of the project, simulation models were developed to analyse how changes in supply and demand affect production planning, economic performance, supply chain relationships, value added, potential product success, market trends and developments and thus competitiveness as measured by the updated [[CPA|FACI]]. One of the outcomes of the research is the Growth risk analyser tool, implemented through simulation/forecasting models for analysing and forecasting pricing trends in the short term: up to 12 months with a confidence band of 95%
 market trends and developments and thus competitiveness as measured by the updated [[CPA FACI]].
   in the case of this tool.
-The tool analyses a time series of data of any value type such as profit per month,
+Forecasts given outside of 12 months become less reliable as the forecast horizon increases (up to 24 months are generated by the tool).
 average sales quantity per day/week/fortnight/etc, average catch/landings per month/semester/year/etc,
-containing a minimum of 24 values (to ensure accuracy).
+The tool analyses a time series of data of any value type such as profit per month, average sales quantity per day/week/fortnight/etc, average catch/landings per month/semester/year/etc, containing a minimum of 24 values (to ensure accuracy).
 == Tool Overview ==
@@ Line 29: / Line 26: @@
 ----
-===Landing page===
+=== Landing page ===
-[[File:Grahome.png| GRA home]]
+The tool is very simple and it's homepage presents the users with two different forms of input for the data to be analysed.
-The input on the left allows the user to upload a file with the time series to be analysed. The file MUST be on
+[[File:Grahome.png|GRA home]]
-value1
+The input on the left allows the user to upload a file with the time series to be analysed. The file MUST be on a .CSV format to ensure compatibility with the forecasting algorithm, containing all the values in the first column, one value per row:
-Each row represents a time point in the time series. You don't need to enter the month for
+value1 value2 value3 value4 ...
-You can create a .CSV file with any spreadsheet editor by simply creating a new spreadsheet and
+Each row represents a time point in the time series. You don't need to enter the month for the values, as each entry is considered a new point value for the month following its preceding value. The results will display the index value of each entry as ordered in the entry data in the X axis starting at 0, and the forecast results will be added as entries at the end of the time series original values.
-[[File:Sprdsheetvalues.png| time series in a spreadsheet]]
+You can create a .CSV file with any spreadsheet editor by simply creating a new spreadsheet and adding the values in the first column, one per row as in the image below:
-After that, click the '''file''' menu of your spreadsheet editor and select '''save as...'''.
+[[File:Sprdsheetvalues.png|time series in a spreadsheet]]
-The input on the left allows you to quickly create a forecast by pasting the values of a time series in the
+After that, click the '''file''' menu of your spreadsheet editor and select '''save as...'''. Once the dialog box for saving opens up, insert the name for the file and select the location. The last and most important and final step is to select '''Text CSV''' as a type in the '''format''' dropdown for the file type which is generally located above the save button. Once you selected the correct type, click the save button and the file will be ready to be uploaded. You can then proceed to the GRA tool home page and click the ''choose file'' button to select your saved file and then click the ''Upload file and calculate forecast'' button to generate your forecast. You can download an example .csv file [[File:Example values.csv|here]].
-===The results===
+The input on the left allows you to quickly create a forecast by pasting the values of a time series in the text box and using it as an input. Each value for the time series must be entered one after the other, separated by a semi-colon '';''. You will get an error message if any of the values are not in the correct format. Once everything is ready click the calculate button below the input to generate the forecast. You can copy and paste the example below in the tool for a demonstration: <code>2500;2750;1598;1777;2100;2107;2213;1993;1768;1479;1344;1389;1433;1477;1578;1611;1678;1587;1523;1124;1388;1299;1433;1399</code>
-Once you click the forecast button you will be redirected to the results page where you will see on the
+=== The results ===
-[[File:Graresult1.png| GRA results page]]
+Once you click the forecast button you will be redirected to the results page where you will see on the left sidebar the current request status and the request input as interpreted by the algorithm once the calculations are complete. It may take a few seconds for the algorithm to run, but you should the see the result graphs.
-The first graph shows the input values and a fitted trend line for the approximation trend generated.
+[[File:Graresult1.png|GRA results page]] [[File:Graresult2.png|GRA results page]] [[File:Graresult3.png|GRA results page]]
 == References & Readings ==
 ----

@@ Line 6: / Line 6: @@
 ----
 == Introduction ==
 ----
-== Methods ==
+== Tool Overview ==
 ----
-&nbsp;
+===Landing page===
-== Models ==
+[[File:Sprdsheetvalues.png| time series in a spreadsheet]]
-----
+After that, click the '''file''' menu of your spreadsheet editor and select '''save as...'''.
-&nbsp;
+The input on the left allows you to quickly create a forecast by pasting the values of a time series in the
-== Reports & Data ==
+Once you click the forecast button you will be redirected to the results page where you will see on the
-----
+[[File:Graresult1.png| GRA results page]]
 == References & Readings ==
 ----

@@ Line 5: / Line 5: @@
 ----
 == Introduction ==
@@ Line 10: / Line 11: @@
 ----
-&nbsp;
+The GRA will predict price over time (up to 12 months) for a species with a 95% confidence interval.
 == Methods ==

@@ Line 31: / Line 31: @@
 ----
-== The tool ==
 ----

@@ Line 26: / Line 26: @@
 &nbsp;
 == Reports &Data ==
 ----

@@ Line 12: / Line 12: @@
 &nbsp;
-:The GRA will predict price over time (up to 12 months)
+:The GRA will predict price over time (up to 12 months) for a species with a 95 confidence interval.
 == Methods ==

@@ Line 1: / Line 1: @@
-= WP2: ECONOMIC PERFORMANCE AND PRICES =
+= Growth Risk Analyser =
 ----
@@ Line 10: / Line 10: @@
 ----
-:WP2 analyses economic performance of the European fisheries and aquaculture sectors using aggregate data obtained from available public sources as well as detailed data from individual companies. This will allow for comparison of the performance of these sectors within Europe, as well as between European countries and other relevant international players. In particular, detailed analysis of growth, productivity and forefront efficiency, using parametric and non-parametric methods, will be conducted on individual European case studies of the chosen species and compared with the performance of Canadian cod and Vietnamese pangasius producers. Additionally, WP2 analyses historically the behaviour of seafood prices in general, as well as the development of market prices of the chosen species, focusing especially on the “boom and bust” cycles characteristics. The outcome of the WP will be critical factors and bottlenecks in the economic performance of the salmon, freshwater trout, cod, sea-bass and bream and herring sectors (SO2).
+&nbsp;
-== Objectives ==
+:The GRA will predict price over time (up to 12 months)
 ----
-*Compare the economic performance of selected European pelagic (herring) fisheries.
+&nbsp;
-== Tasks ==
+== Models ==
 ----
-#Economic performance of selected individual sector.
+&nbsp;
-#*Demersal (cod) fisheries
-#*North Atlantic pelagics (herring)
+== Reports &Data ==
-#*Freshwater trout
-#*Atlantic salmon
+----
-== Deliverables ==
+== The tool ==
 ----
-*Report on the development of prices & volumes in the European fishery & aquaculture market
+== References & Readings ==
-*Report on the economic performance of selected European and Canadian fisheries
-*Report on the economic performance of selected European and Vietnamese farmed species
+----