🕷️ Crawler Inspector

URL Lookup

Direct Parameter Lookup

Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 31 (from laksa045)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa031.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, meta_canonical AS meta_canonical, ml_categories_json AS ml_categories_json, ml_types_json AS ml_types_json, ml_intent_types_json AS ml_intent_types_json, meta_language AS meta_language, attrs_author AS attrs_author, ifNull(toUnixTimestamp(attrs_publish_time), 0) AS attrs_publish_time, ifNull(toUnixTimestamp(attrs_original_publish_time), 0) AS attrs_original_publish_time, ifNull(attrs_is_republished, 0) AS attrs_is_republished, ifNull(attrs_nr_words, 0) AS attrs_nr_words, ifNull(attrs_boilerpipe_nr_words, 0) AS attrs_boilerpipe_nr_words, ifNull(body_ext_links_number, 0) AS body_ext_links_number, ifNull(body_int_links_number, 0) AS body_int_links_number, ifNull(meta_nofollow, 0) AS meta_nofollow, ifNull(meta_noarchive, 0) AS meta_noarchive, ifNull(props_was_rendered, 0) AS props_was_rendered, ifNull(src_redirect, \'\') AS src_redirect, ifNull(download_time_msec, 0) AS download_time_msec, ifNull(download_ttfb_msec, 0) AS download_ttfb_msec, ifNull(download_size, 0) AS download_size FROM crawler3.page_info_local FINAL PREWHERE int_partition_id = 55 AND (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.mdpi.com/2504-3110/10/4/218\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.mdpi.com/2504-3110/10/4/218\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/www.mdpi.com\/2504-3110\/10\/4\/218","crawl_time":1780294825,"first_indexed_time":1774575776,"http_code":200,"src_unparsed":"com,mdpi!www,\/2504-3110\/10\/4\/218 s443","src_root_hash":"11974975279136771031","history_drop_reason":null,"meta_title":"Cryptocurrency Price Prediction Using Sliding Empirical Mode Decomposition with Economic Variables: A Machine Learning Approach","meta_descriptions":["The cryptocurrency market has attracted significant attention from global investors, with Cardano (ADA) ranking among the top cryptocurrencies by market capitalization. However, predicting ADA returns remains challenging due to the complex, multi-scale dynamics influenced by Federal Reserve policies, geopolitical events, and high-frequency trading. This study proposes a “Sliding EMD–Multi Variables” framework for cryptocurrency return prediction, leveraging Empirical Mode Decomposition’s multi-scale fractal properties to capture nonlinear dynamics at different time scales. The sliding window decomposition method addresses data leakage issues while incorporating key economic and policy variables at the component level. The empirical results demonstrate that the Sliding EMD system significantly outperforms univariate and multivariate benchmarks. Compared to the univariate system, it improves MSE, RMSE, SMAPE, and DSTAT by 0.83%, 0.42%, 5.23%, and 0.43%, respectively, while enhancing investment metrics (maximum drawdown, Sharpe ratio, Sortino ratio, Calmar ratio) by 0.19, 0.36, 0.95, and 0.15. Against the multivariate system, improvements reach 5.52%, 3.14%, 5.74%, and 17.62% in prediction accuracy, with investment performance gains of 0.47, 1.69, 4.27, and 0.31. Incorporating economic variables at the component level yields additional improvements of 0.94%, 0.47%, and 0.78% in MSE, RMSE, and MAE. These findings offer valuable insights for cryptocurrency portfolio optimization using fractal-based decomposition methods."],"meta_canonical":null,"ml_categories_json":"{\"\/Finance\":991,\"\/Finance\/Investing\":986,\"\/Finance\/Investing\/Currencies_and_Foreign_Exchange\":337,\"\/Science\":246,\"\/Science\/Computer_Science\":241,\"\/Science\/Computer_Science\/Machine_Learning_and_Artificial_Intelligence\":231,\"\/Computers_and_Electronics\":110,\"\/Computers_and_Electronics\/Software\":101}","ml_types_json":"{\"\/Article\":990,\"\/Article\/Study_or_Research_Findings\":983}","ml_intent_types_json":"{\"Informational\":999}","meta_language":"en","attrs_author":null,"attrs_publish_time":0,"attrs_original_publish_time":1774575776,"attrs_is_republished":0,"attrs_nr_words":"20455","attrs_boilerpipe_nr_words":"15075","body_ext_links_number":220,"body_int_links_number":122,"meta_nofollow":0,"meta_noarchive":0,"props_was_rendered":1,"src_redirect":"","download_time_msec":156,"download_ttfb_msec":145,"download_size":99155}

3. Robots.txt Check

Query:

Response:

4. Spam/Ban Check

Query:

Response:

5. Seen Status Check

ℹ️ Skipped - page is already crawled

📍

LOCATION

Host 31 · Partition 55

laksa031

11974975279136771031

📄

INDEXABLE

✅

CRAWLED

2 days ago

🤖

ROBOTS ALLOWED

Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	0.1 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Page Details

Property

Value

URL

https://www.mdpi.com/2504-3110/10/4/218

Last Crawled

2026-06-01 06:20:25 (2 days ago)

First Indexed

2026-03-27 01:42:56 (2 months ago)

HTTP Status Code

200

Content

Meta Title

Cryptocurrency Price Prediction Using Sliding Empirical Mode Decomposition with Economic Variables: A Machine Learning Approach

Meta Description

The cryptocurrency market has attracted significant attention from global investors, with Cardano (ADA) ranking among the top cryptocurrencies by market capitalization. However, predicting ADA returns remains challenging due to the complex, multi-scale dynamics influenced by Federal Reserve policies, geopolitical events, and high-frequency trading. This study proposes a “Sliding EMD–Multi Variables” framework for cryptocurrency return prediction, leveraging Empirical Mode Decomposition’s multi-scale fractal properties to capture nonlinear dynamics at different time scales. The sliding window decomposition method addresses data leakage issues while incorporating key economic and policy variables at the component level. The empirical results demonstrate that the Sliding EMD system significantly outperforms univariate and multivariate benchmarks. Compared to the univariate system, it improves MSE, RMSE, SMAPE, and DSTAT by 0.83%, 0.42%, 5.23%, and 0.43%, respectively, while enhancing investment metrics (maximum drawdown, Sharpe ratio, Sortino ratio, Calmar ratio) by 0.19, 0.36, 0.95, and 0.15. Against the multivariate system, improvements reach 5.52%, 3.14%, 5.74%, and 17.62% in prediction accuracy, with investment performance gains of 0.47, 1.69, 4.27, and 0.31. Incorporating economic variables at the component level yields additional improvements of 0.94%, 0.47%, and 0.78% in MSE, RMSE, and MAE. These findings offer valuable insights for cryptocurrency portfolio optimization using fractal-based decomposition methods.

Meta Canonical

null

Boilerpipe Text

heavy column, fetched on demand

Markdown

heavy column, fetched on demand

Readable Markdown

heavy column, fetched on demand

ML Classification

ML Categories

/Finance		99.1%
/Finance/Investing		98.6%
/Finance/Investing/Currencies_and_Foreign_Exchange		33.7%
/Science		24.6%
/Science/Computer_Science		24.1%
/Science/Computer_Science/Machine_Learning_and_Artificial_Intelligence		23.1%
/Computers_and_Electronics		11.0%
/Computers_and_Electronics/Software		10.1%

Raw JSON

{
    "/Finance": 991,
    "/Finance/Investing": 986,
    "/Finance/Investing/Currencies_and_Foreign_Exchange": 337,
    "/Science": 246,
    "/Science/Computer_Science": 241,
    "/Science/Computer_Science/Machine_Learning_and_Artificial_Intelligence": 231,
    "/Computers_and_Electronics": 110,
    "/Computers_and_Electronics/Software": 101
}

ML Page Types

/Article		99.0%
/Article/Study_or_Research_Findings		98.3%

Raw JSON

{
    "/Article": 990,
    "/Article/Study_or_Research_Findings": 983
}

ML Intent Types

Informational

99.9%

Raw JSON

{
    "Informational": 999
}

Content Metadata

Language

Author

null

Publish Time

not set

Original Publish Time

2026-03-27 01:42:56 (2 months ago)

Republished

Word Count (Total)

20,455

Word Count (Content)

15,075

Links

External Links

220

Internal Links

122

Technical SEO

Meta Nofollow

Meta Noarchive

JS Rendered

Yes

Redirect Target

null

Performance

Download Time (ms)

156

TTFB (ms)

145

Download Size (bytes)

99,155

Location

Host ID

31 (laksa031)

Partition ID

Root Hash

11974975279136771031

Unparsed URL

com,mdpi!www,/2504-3110/10/4/218 s443