6680691 Ashita Financial Time Series

Preprocessing Data

String to Date&Time — the Date column starts as plain text (like "06/21/2026"). This node turns it into an actual date type, help KNIME understands it as a calendar date, not just a word.

2. Three parallel "assembly lines" for the Volume column
Raw Volume numbers come in three different shorthand styles: 12.4K, 3.1M, 1.2B (thousands/millions/billions). A computer can't do math on text like "12.4K", built one branch for each suffix:

• Row Filter — pulls out only the rows ending in that particular letter (K, M, or B)

• String Replacer — deletes the letter, leaving just the number as text (e.g. "12.4")

• String to Number — converts that text into an actual number KNIME can calculate with

• Math Formula — multiplies it back up to the true value (e.g. ×1,000 for K)

I run three copies of this same four-step process side by side because each suffix needs a different multiplier for it to work.

3. Stitch it back together

• Concatenate — Connect the three branches back into one table now that Volume is a real number everywhere

• String Replacer — removes the "%" symbol off the Change % column

• String to Number — converts that into a real number too

• Math Formula — divides by 100, turning "3.5%" into 0.035 (a decimal), which is the standard way to represent a percentage for calculations

4. Final tidy-up data to be use for other workflow

• Sorter — puts all rows in date order

• Duplicate Row Filter — removes any accidental duplicate dates

• Component Output — hands the finished clean table out to the rest of the workflow

• Statistics — produces a quick summary table (min/max/average/etc. for each column)

The preprocessing data is more accurate and consistent because it ensure the correct date format, no duplicate dates, corrected outliers, and only the necessary columns (Date and Price).

Visualization Data

Branch 1 (top) — basic visualization of the data

• Line Plot "Price" → raw price over time

• Line Plot "Log of Price" → log-transformed price over time (raw vs. transformed comparison)

• Histogram "Price" → how price values are distributed

• Histogram "Return" → how daily % moves are distributed (spikes here = outlier days)

Branch 2 (middle) — seasonality, static charts

• Extract a calendar unit (e.g. month) → do a calculation → Pivot into a comparable layout → Line Plot it

• Repeated a second time for a different calendar unit (e.g. year)

Branch 3 (bottom) — seasonality, one interactive dashboard (Candlestick)

• Extract a calendar unit → combine columns into a label (e.g. "2024-03") → GroupBy to average per period → rename columns → sort → render as an interactive chart (ECharts)

Top branch shows what the raw data looks like and flags outlier days; middle and bottom branches both check for seasonal patterns.

This component mainly focus on exploratory visualizations through raw-vs-log price comparison, return/price distributions, and two different seasonality breakdowns (one as static line plots, one as an interactive dashboard) in order to visually inspect the data and spot outliers before modeling.

Major Macroeconomic Shocks & Market Events (Observed from visualization data)

2017 bubble & crash: It could be observed from the price and log price chart that there is a sharp rise to $1,000+ during late 2017, then crash into 2018 due to "crypto winter"

2019-2020 slow grind: It could be observed from the chart that there is a Long flat/low period (~$100-300) before breakout in late 2020, aligning with DeFi summer momentum carrying into 2021.

2022 bear market: It could be observed from the candlestick chart that there is a steady decline from $4,800 down to $1,000 due to Luna and FTX collapses.

From the histogram chart for price. The big spikes at $6.7 is not a real outliers as it just reflects ETH years of low early prices

From the histogram chart for return. There is a peak near 0, with a long left tail out to -0.35 to -0.59, suggesting extreme crash days far outside normal daily volatility, is it likely link with known crashes such as the 2018 bear market and 2020 Luna and FTX collapses incident.

Outlier detection -> catch errors and maintain data quality

Outlier detection with rolling statistics : Used the moving aggregator to find the mean and standard deviation for Price. Then used the math formula in order to find both Upper limit and Lower limit. Lastly, used the rule engine node to flag the outliers. If the $Price is more than the UCL and lower than the LCL, it is considered as an outliers

Outlier detection and Hampel Filter Techniques: Used the moving aggregator to find the median. Then using the moving aggregator to find the median of absolute. After that used the math formula as to look for the control limit. Lastly, used the rule engine to flag for noticeable outliers. If the absoluate_deviation$ > $control_limit$ then it is an "outlier"

For this project,Hampel Filter with a rolling median is being used, where any noticeable outliers were then replaced with the rolling median. This made the data cleaner and helped KNIME produce more accurate forecasts which will later be used for ARIMA and Holt’s Exponential Smoothing models.

Trend and Momentum

Using Rule Engine Node to determine the strength of the trend based on the Hurst Exponent and Z-Score

$Hurst Exponent$ > 0.55 AND $MK_Z_Score$ > 1.96 => "Strong Bullish Trend"

$Hurst Exponent$ > 0.55 AND $MK_Z_Score$ < -1.96 => "Strong Bearish Trend"

$Hurst Exponent$ < 0.45 AND ($MK_Z_Score$ > -1.96 AND $MK_Z_Score$ < 1.96) => "Mean Reversion / Sideways"

$Hurst Exponent$ < 0.45 AND ($MK_Z_Score$ < -1.96 OR $MK_Z_Score$ > 1.96) => "Trend Exhuasion Warning"

Most periods show a clear trend because it could be observed that the Hurst Exponent is above 0.5 in all rows (around 0.66–0.80). This means the ETH price tends to continue moving in the same direction and is not always random.

Lag Column and Math formula before the outlier detection process, the price is converted using a logarithm ln($Price$/$Price(-1)$) as shown by the new added column Price(-1).