Hey everyone,

I am kind of desperate for help right now on my current project. I'll try and be as clear as possible.

I'm working on a time series backcasting problem. The values I want to backcast are forecasts (not ML forecast, but think of weather forecasts) at different horizon (from 1 to 14). So to be clear, at a date D, I have 14 forecasts (forecast at D+1,..., D+14). I have such forecasts from 2020 to 2026 (each row represents a day, each (date, horizon) key is unique). So I have 14 dates duplicated as blocks because each row consists of on unique(date, horizon) -> target_date. I hope this is clear enough.

So the goal is to backcast those forecasts before 2020 (say 2019-2020 for simplicity). Besides forecasts values and horizon columns, I have "actuals" that are the true measured values for a particular variable (say temperature), and "normals" which is a smooth curves representing the climatology norm for a particular data. This "normals" column captures the seasonality, trend, and every other repetitive and predictable patterns.

So to be clear I have :

* dates (of forecast emission) | actuals | normals | horizon | forecasts *

And to really emphasise this point : dates, actuals and normals are the same for 14 consecutive rows (One row equals one horizon).

The target I want to predict is the following : forecast - actual_at_forecast_date

So i want to predict the true error observed (say i had predicted 20 (forecast) for today and I measure 18 (actual) then my target is +2).

So far, I've done the following :

- Transform target to remove annual seasonality, long-term trend and level-scaling

- Engineered classic features such as anomaly (actual-normal), lagged anomalies, rolling stats (std, mean, median, quantiles)

- Engineered target encoding features such as target_encoding_horizon_x_month

- RandomForest with max_depth 10-15, min_leaf 10, max features "sqrt", n_estimators 300

My train/val folds are reversed because I wanted to best evaluate on a backcasting framework. I made sure there is no leakage.

FINALLY:

My main problem is that, even with a LOT of features combination, trying a LOT of tuning, my prediction is very shallow and shrinking to the mean (the std and q10, q90 are off by a lot). So given I try to predict forecast_error which is centered on 0, I start to think that I only capture noise because my predictions really won't fit anything. MAE is getting worse with higher horizon forecasts which is only natural but even for horizon 1 my prediction is as good as predicting only 0s MAE-wised. Please if anyone has ideas that I can explore on my own I would be so grateful. I know you don't have all the details here but if you have experience with backcasting and has some recommendations I would be so grateful.

Hey everyone,

I'm working on a time series backcasting problem and I'm running into a fairly stubborn issue. I'd really appreciate any insights from people who have worked on similar setups.

Problem setup

I have daily-issued forecasts with multiple horizons:

• At each date D, I have forecasts for D+1, ..., D+14
• Data spans 2020–2026
• Each row is a unique (forecast_date, horizon) pair

Toy example:

forecast_date	horizon	target_date	forecast	actual	normal
2023-01-01	1	2023-01-02	20	18	19
2023-01-01	2	2023-01-03	21	20	19
...	...	...	...	...	...
2023-01-01	14	2023-01-15	25	23	20

Important:

• forecast_date, actual, and normal are identical across the 14 horizons
• Only horizon, target_date, and forecast vary

Objective

I want to backcast forecast errors before 2020.

Target:

target = forecast − actual(target_date) 

So if forecast = 20 and actual = 18 → target = +2.

Features

• forecast, horizon
• actual, normal
• anomaly = actual − normal
• lagged anomalies
• rolling stats (mean, std, quantiles)
• target encoding (e.g. horizon × month)

Model

Random Forest:

• max_depth: 10–15
• min_samples_leaf: 10
• max_features: sqrt
• n_estimators: 300

Validation

• Time-based splits adapted for backcasting
• No leakage (checked carefully)

Main issue

Predictions are very shallow and collapse toward 0:

• Very low variance
• Poor estimation of tails (q10 / q90)
• Even for horizon = 1, performance is close to predicting constant 0 (in MAE)

MAE increases with horizon (expected), but overall performance remains weak.

Diagnostics

• std(predictions) / std(target) ≈ 0.4 at best
• This ratio decreases with horizon

So the model is clearly under-dispersed.

Interpretation

At this point I suspect:

• either the signal is very weak
• or the model is too conservative and fails to capture amplitude

Any help, feedback, or ideas to explore would be greatly appreciated.

Thanks a lot.