Analytics and forecasts

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Forecasting

Zoho Analytics enables you to effectively predict your future data trends, using its forecasting feature. Forecasting in Zoho Analytics is based on powerful forecasting algorithms which analyze your past data deeply and come up with the best forecast for the future. You can set up a forecast in a chart using a very simple set up process, without worrying about the underlying complexity.

This document will help you to learn how forecasting works in Zoho Analytics and how you can easily set up the same in your charts.

Forecasting

Troubleshooting Tips

1. What is forecasting?

Forecasting is a process of predicting the future based on the past data trend. Zoho Analytics forecasting is based on powerful forecasting algorithms which analyzes your past data deeply and comes up with the best forecast for the future.

2. What are the chart types that support Forecasting?

Zoho Analytics supports forecasting for the following chart types.В

  • Line Chart
  • Bar Chart
  • Stacked Bar Chart
  • Scatter Chart
  • Area Chart
  • Stacked Area Chart
  • Combo Charts (without Bubble Chart).

3. How do I setup Forecasting in my chart?

You can set up forecasting using the Chart Settings page. To set up forecasting in a chart, follow the below steps to set up Forecasting in your chart.

  1. Open the Chart in which you want to set up the forecast. Refer here to know forecasting is available for the chart.В
  2. Click the Settings icon. The ChartsВ Settings page will open.
  3. Open the Forecast tab.В
  4. Click Add Forecast. All the possible Y-axis series to be forecasted will be listed.
  5. Select the Y-axis series to be forecasted. The forecast setting will be displayed.В
  6. In the Forecast Length, select the number of points to be forecasted.
  7. In the Ignore Last,В select the number of points to be ignored from the past starting from the current point.
  8. In the Prediction Interval, specify the confidence interval in which the data point is most likely to occur.В Select the interval ranging from 75 to 95. You can also choose to set this as none. This option is applicable for line chart alone.
  9. Specify the legend title forВ the forecasted data series in the Legend Name
  10. Specify the Formatting to be applied to show the forecasted data points.
  11. Click Apply. The forecasted series will be added to the chart.

The forecasted data series will be listed as a Legend Item in the chart. This allows you to view or remove the forecasted points in the chart easily.

4. Can I set up forecasting over multiple Y-Axis?

Yes, you can set up forecasting over multiple Y-Axis in a chart.В

5. Forecast option is not available in my Chart settings, why?

Forecasting will be enabled when it matches certain conditions, which are briefed below:

  • The chart type should be any one of the following:
    Line Chart, Bar Chart, Stacked Bar Chart, Scatter Chart, Area Chart, Stacked Area Chart, Web Chart, Combo Charts.
  • The X-axis of the chart should be a TIME series or NUMBER series.
  • Charts should have only one dimension column in X-axis: Forecasting will be enabled only when the chart has a single dimension column (in X-axis). If the chart contains Text, Color, Size or Tooltip, then forecasting will not be enabled.
  • Atleast one aggregate function should be present in Y-axis: Forecasting can be applied over the aggregate function alone. if all the Y-axis series in the chart contain advanced summarizing options like running total, then forecasting will not be enabled.
  • If the chart is filtered with a numeric column, then forecasting will be disabled. The forecasting based on filtered data might give inaccurate results, hence forecasting is disabled.
    ​

6. How Forecasting works in Zoho Analytics?

Zoho Analytics offers a powerful forecasting engine which predicts future data points based on past data. The forecasting engine offers a range of customizations such as number of units to be forecasted, number of data points to be ignored in the past data and the formatting to be applied over the forecasted data points.

The following points describe how the forecasting engine works in Zoho Analytics:

  1. The forecasting engine analyses past data points.
  2. Based on the past data, the forecasting engine will identify the periodicity using auto-correlation method.
  3. Then it computes the seasonality, trend and randomness using the past data.
  4. By iterative processing, the forecasting engine fine-tunes the computed seasonality, trend and randomness.
  5. The engine runs linear, logarithmic and exponential regressions and identifies the data series falls under linear, logarithmic or exponential.
  6. The accuracy of the predicted results will be verified using Hindcasting. Hindcasting is the process of predicting the past data points and verify the same with the actual points.
  7. Once all verifications are done, the forecasting engine produces the final forecasted points

7. The forecasted points shown in chart are different to the shared users. Why?

The chart could have been shared with different filter criteria to the shared users. Number of past data points available in the shared data could very for different shared users, hence the forecasted points are different for shared users.

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8. The underlying data and drill down options are not available for forecasted data points. Why?

The forecasted points will not have underlying data generated for each of the forcasted data points. Hence, View Underlying Data and Drill Down options will not be available for forecasted data points.

Troubleshooting Tips

1. I could not find “Forecast” option in chart settings. Why?

This could happen when the forecast constraints are not met. Please refer to the constraints specified.

2. Already configured forecasting settings are not available now. Why?

This could happen when the design of the chart has been modified, which does not match the forecast constraints.

3. I have configured forecasting for my chart. But it says, “Forecasting is disabled as the data is completely ignored”. Why?

This could happen if you had set to ignore all the past data points from “Ignore Last” setting.

4. I have configured forecasting for my chart. But it says, “Forecasting is disabled as there is not enough data to identify pattern”. Why?

This could happen when there is no sufficient data produced to forecasting engine to come up with forecasted data points.

5. I have configured forecasting for my chart. But it says, “Forecasting is disabled as there are more than 40% empty values”. Why?

When the past data points provided to the forecasting engine has more null values, the forecasted points might be inaccurate. To avoid this, the forecasting engine will discard the process when the null values are more than 40% in the given data.

6. I have configured forecasting for my chart. But it says, “Column cannot be forecast as more than 5 data points is required”. Why?

To produce an accurate forecast, the data points to be considered for forecasting should be more than 5 points. Try changing the time series in X-axis to a more granular function which may result in more data points. For example, If the existing time series is Year, then change to Month & Year.

Analytics Magazine

The Dow Chemical approach to leveraging time-series data and demand sensing.

By Tim Rey (Left) and Chip Wells

Big data means different things to different people. In the context of forecasting, the savvy decision-maker needs to find ways to derive value from big data. Data mining for forecasting offers the opportunity to leverage the numerous sources of time series data, both internal and external, now readily available to the business decision-maker, into actionable strategies that can directly impact profitability. Deciding what to make, when to make it and for whom is a complex process. Understanding what factors drive demand, and how these factors (e.g., raw materials, logistics, labor, etc.) interact with production processes or demand and change over time are keys to deriving value in this context.

The Dow Chemical Company was interested in developing an approach for demand sensing that would provide:

  • reduction in resource expenses for data collection and presentation
  • consistent automated source of data for leading indicator trends

Agility in the Market

  • shifting to external and future looks from internal history
  • broader dissemination of key leading indicator data
  • better timing on market trends … faster price responses, better resource planning (by reducing allocation/force major/share loss on the up side and reducing inventory carrying costs and asset costs on the down side)
  • accuracy of timing and estimates for forecast models
  • understanding leading indicator relationships

Figure 1: Levels of hierarchy at Dow Chemical.

Dow (and its Advanced Analytics team) was keenly interested in better forecasting models for volume (demand), net sales, standard margin, inventory costs, asset utilization and EBIT (earnings before interest and taxes). This was to be done for all businesses and all geographies. Similar to many large corporations, Dow has a complex business/product hierarchy. This hierarchy starts at the top, total Dow, then moves down through divisions, business groups, global business units, value centers, performance centers, etc. As is the case in most large corporations, this hierarchy is always changing and is overlaid with geography. Even lower levels of the hierarchy exist when specific products are considered.

Dow operates in the vast majority of the 16 global market segments as defined in the ISIC (International Standard Industrial Classification) market segment structure, some of which are: agriculture, hunting and forestry, mining and quarrying, manufacturing, electricity, gas and water supply, construction, wholesale and retail trade, hotels and restaurants, transport, storage and communications, health and social work, etc. This includes commodities, differentiated commodities and specialty products and thus makes the mix even more complex. The value chains Dow is involved in are very deep and complex, and often connect the earliest stages of hydrocarbons extraction and production all the way to the consumer on the street.

Figure 2: Dow’s value chains are deep and complex.

Before embarking on the project, the team contemplated a few “industrial” and economic considerations to attack. First, simply multiplying out the number of models, the team saw that they would have around 7,000 exogenous variable models to build, so we focused on the top global business units (by area combinations in each division, restricting our initial effort to covering 80 percent of net sales). Next, we realized that the target variables of interest (volume, asset utilization, net sales, standard margin, inventory costs and EBIT) are generally related to one another. Thus, volume is a function of volume “drivers” (Vx), represented by f(Vx); asset utilization (AU) is a function of volume and AU “drivers” f(AUx); inventory is a function of volume and inventory (INV) “drivers” f(INVx); net sales is driven by volume, various costs (xcosts) and net sales “drivers” f(NSx); standard margin is driven by net sales and standard margin “drivers” f(SMx); and finally EBIT is driven by standard margin and EBIT “drivers” f(EBITx).

The problem, if done only at one level of the hierarchy, fits into a multivariate in Y approach that could be solved using a VARMAX (vector auto regressive moving average with exogenous variables) system. The complexity here is that we needed to solve the problem across the hierarchy shown above. We proposed that we could mimic the VARMAX structure by building the models in a “daisy chain” fashion shown in Figure 3. As a baseline, we thus compared a traditional VARMAX approach to the daisy chain approach at the total Dow level. We also did a traditional univariate model, as well as a traditional ARIMAX model for each Y. The “Reconciled” column in Table 1 was the daisy chain approach used in the hierarchy (implemented via SAS Forecast Studio) and then reconciled up. Given the results in Table 1, we were confident we could use the daisy chain approach across the hierarchy and get similar benefit to the VARMAX approach. All of the above was accomplished with various SAS forecasting platforms.

Figure 3: Target variables of interest are generally related to one another.



Table 1: SAS Forecast Studio screen shot.

Following the data mining for forecasting process described in “Applied Data Mining For Forecasting Using SAS” (Rey, Kordon and Wells (2020)) – Chapters 2 and then 7 – which covers exogenous variable identification and then Reduction and Selection for forecasting leads to conducting dozens of mind mapping sessions to have the businesses propose various sets of “drivers” for the numerous GBU and VC by geographic area combinations. This leads to using thousands (more than 15,000 in this case!) of potential exogenous variables of interest for the 7,000 models in the hierarchy. This is truly a big data, large-scale forecasting problem. A lot of automation was necessary for first setting up initial research projects, as well as automatically building initial univariate and daisy chain models.

Lastly, concerning visualization, the business can gain access to these forecasts in a corporate-wide business intelligence delivery system where they can see the history, model, forecast, confidence limits and drivers.

Big data mandates big judgment. Big judgment has to have short “ask-to-answer” cycles. These opportunities call for the use of data mining for forecasting approaches that lead to using special techniques for variable reduction and selection on time series data.

Analytics and forecasts

The BRC Retail Sales Monitor is an accurate monthly measure of retail sales performance that acts as both a benchmark for participating retailers and as a key economic indicator.

The RSM measures changes in the actual value of retail sales based on figures supplied directly by participating members. Originally set up at the request of BRC members to benchmark their own business performance against the wider sector, the BRC Retail Sales Monitor is an authoritative measure on the health of the UK retail sector and the wider economy.

Forecast

Importance

0.2% Reading 1.0%

The average amount of pre-tax earnings per regular employee, including overtime pay and bonuses.

Forecast

Importance

-3.3% Reading -0.3%

A survey of both wage-earning and non-working households, such as those classified as single-member, unemployed, or retired. The headline figure is the percentage change in average spending per household from the previous year. Increases in household spending are favorable for the Japanese economy because high consumer spending generally leads to higher levels of economic growth. Higher spending is also a sign of consumer optimism, as households confident in their future outlook will spend more. At the same time accelerated growth exerts inflationary pressure, which can lead to interest rate increases in the future.

Forecast

Importance

Reading -10.3%

The ANZ job advertisement series measures the number of jobs advertised in the major daily newspapers and Internet sites covering the capital cities each month.

Forecast

Importance

3.75bln Reading 4.36bln

A country’s trade balance reflects the difference between exports and imports of goods and services. The trade balance is one of the biggest components of the Balance of Payment, giving valuable insight into pressures on country’s currency.

Surpluses and Deficits
A positive Trade Balance (surplus) indicates that exports are greater than imports. When imports exceed exports, the country experiences a trade deficit. Because foreign goods are usually purchased using foreign currency, trade deficits usually reflect currency leaking out of the country. Such currency outflows may lead to a natural depreciation unless countered by comparable capital inflows (inflows in the form of investments, FDI – where foreigners investing in local equity, bond or real estates markets). At a bare minimum, deficits fundamentally weigh down the value of the currency.

Ramifications of Trade Balance on Markets
There are a number of factors that work to diminish the market impact of Trade Balance upon immediate release. The report is not very timely, coming some time after the reporting period. Developments in many of the figure’s components are also typically anticipated well beforehand. Lastly, since the report reflects data for a specific reporting month or quarter, any significant changes in the Trade Balance should plausibly have already been felt during that period, not during the release of data.

However, because of the overall significance of Trade Balance data in forecasting trends on Forex, the release has historically been one of the more important reports in any country.

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