Forecast data error - timeline not consistent

[Shravan133]

Hi, 

I am trying to forecast growth of DB size by dates in a Line Chart. But i get an error saying the timeline is not consistent. Is there a solution to this?

[Anonymous · Accepted solution]

Hi Ritaf1983 ,thanks for the quick reply, I'll add more.

Hi @Shravan133 ,

Regarding your problem, I think the problem occurs because the date column in your original data is not continuous.The forecast function requires consecutive dates.

You need to create a date table containing consecutive dates and merge the data from your original data table into it.

Table 2 = 
ADDCOLUMNS(CALENDAR(DATE(2024,1,1),DATE(2024,12,31)),"value",
VAR _result = SUMX(FILTER('Table',[Date] = EARLIER([Date])),[Value]) RETURN IF( ISBLANK(_result),0,_result))

Best Regards,
Wenbin Zhou

[Ritaf1983]

Hi @Shravan133 

Based on the image you’ve shared, it looks like the line chart is displaying some unexpected patterns. The zigzag pattern and sharp drops or spikes in the data suggest there might be some issues related to the data itself.

Observations:

1. Erratic Pattern: The chart shows a zigzag pattern, which could indicate that the data includes sudden increases or decreases. This may be due to anomalies or irregularities in the data.

2. Negative Growth Values: There are negative values in the chart, which could suggest that the calculation of growth sometimes results in negative numbers. This might happen due to data entry issues, unexpected data points, or an actual decrease in the database size.

Potential Solutions:

1. Review the Data for Anomalies:

   • Check the data to ensure there are no gaps, sudden spikes, or drops that might be causing these unusual patterns. It’s important to verify that the dates and corresponding growth values are accurate and consistent.

2. Consider a Cumulative Sum:

   • Instead of plotting individual growth values, you might want to consider using a cumulative sum of growth over time. This can provide a clearer view of the overall trend and make it easier to identify long-term patterns rather than focusing on individual fluctuations.

3. Data Smoothing Techniques:

   • If the fluctuations are too pronounced, applying a smoothing technique, such as a moving average, might help. This approach can make trends more apparent and reduce the visual noise caused by erratic data points.

4. Investigate Outliers:

   • Take a closer look at any outliers or unusual data points that could be skewing the visualization. You might temporarily exclude these outliers to see if the overall trend becomes more stable.
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