Time-series forecasting with long-range, multi-scale dependencies remains challenging. Large language models (LLMs) are promising for sequence reasoning; however, direct prompting often struggles with rapidly varying dynamics and underutilizes frequency-domain structure, while full fine-tuning is costly. We present tsLLM-SG, a fine-tuning framework that combines Dynamic Soft Prompts (DSP) with Gated Frequency Transformation Adapters (GFTA). DSP uses lightweight, learnable embeddings to capture statistical cues and frequency-domain distributions from the input sequence and injects these prompts into the token stream, providing the LLM with critical task-specific context. GFTA acts as a residual spectral filter that adaptively modulates token representations in the frequency domain. On long-horizon and few-shot forecasting benchmarks, tsLLM-SG attains higher accuracy with substantially fewer trainable parameters than recent LLM-based baselines. Ablations and visualizations indicate that DSP and GFTA deliver complementary gains, strengthening temporal representations. Overall, tsLLM-SG offers a scalable, computationally efficient way to leverage LLMs for time-series forecasting.